Adaptive phenotypic divergence in an annual grass differs across biotic contexts*
Bibliographic record
Abstract
Climate is a powerful force shaping adaptation within species, yet adaptation to climate occurs against a biotic background: species interactions can filter fitness consequences of genetic variation by altering phenotypic expression of genotypes. We investigated this process using populations of teosinte, a wild annual grass related to maize (Zea mays ssp. mexicana), sampling plants from 10 sites along an elevational gradient as well as rhizosphere biota from three of those sites. We grew half-sibling teosinte families in each biota to test whether trait divergence among teosinte populations reflects adaptation or drift, and whether rhizosphere biota affect expression of diverged traits. We further assayed the influence of rhizosphere biota on contemporary additive genetic variation. We found that adaptation across environment shaped divergence of some traits, particularly flowering time and root biomass. We also observed that different rhizosphere biota shifted expressed values of these traits within teosinte populations and families and altered within-population genetic variance and covariance. In sum, our results imply that changes in trait expression and covariance elicited by rhizosphere communities could have played a historical role in teosinte adaptation to environments and that they are likely to play a role in the response to future selection.
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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".