WATER STRESS ALTERS THE GENETIC ARCHITECTURE OF FUNCTIONAL TRAITS ASSOCIATED WITH DROUGHT ADAPTATION IN<i>AVENA BARBATA</i>
Bibliographic record
Abstract
Environmental stress can alter genetic variation and covariation underlying functional traits, and thus affect adaptive evolution in response to natural selection. However, the genetic basis of functional traits is rarely examined in contrasting resource environments, and consequently, there is no consensus regarding whether environmental stress constrains or facilitates adaptive evolution. We tested whether resource availability affects genetic variation for and covariation among seven physiological traits and seven morphological/performance traits by growing the annual grass Avena barbata in dry and well-watered treatments. We found that differences in the overall genetic variance-covariance (G) matrix between environments were driven by physiological traits rather than morphology and performance traits. More physiological traits were heritable in the dry treatment than the well-watered treatment and many of the genetic correlations among physiological traits were environment dependent. In contrast, genetic variation and covariation among the morphological and performance traits did not differ across treatments. Furthermore, genetic correlations between physiology and performance were stronger in the dry treatment, which contributed to differences in the overall G-matrix. Our results therefore suggest that physiological adaptation would be constrained by low heritable variation in resource-rich environments, but facilitated by higher heritable variation and stronger genetic correlations with performance traits in resource-poor environments.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".