Independent evolution of ab- and adaxial stomatal density enables adaptation
Bibliographic record
Abstract
Are organisms free to reach their adaptive optima or constrained by hard-wired developmental programs? Recent evidence suggests that the arrangement of stomata on abaxial (lower) and adaxial (upper) leaf surfaces may be an important adaptation in plants, but stomatal traits on each surface likely share developmental pathways that could hamper evolution. We reviewed the quantitative genetics of stomatal density to look for loci that (1) affected ab- or adaxial density independently or (2) pleiotropically affected stomatal density on both surfaces. We also used phylogenetic comparative methods to test for independent versus correlated evolution of stomatal traits (density, size, and pore index) on each surface from 14 amphistomatous wild tomato taxa ( Solanum ; Solanaceae). Naturally occurring and laboratory-induced genetic variation alters stomatal density on one surface without affecting the other, indicating that development does not strongly constrain the spectrum of available mutations. Among wild tomato taxa, traits most closely related to function (stomatal pore index and density) evolved independently on each surface, whereas stomatal size was constrained by correlated evolution. Genetics and phylogenetics demonstrate mostly independent evolution of stomatal function on each leaf surface, facilitating largely unfettered access to fitness optima.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".