Selection constrains phenotypic evolution in a functionally important plant trait
Bibliographic record
Abstract
Abstract A long-standing idea is that the macroevolutionary adaptive landscape – a ‘map’ of phenotype to fitness – constrains evolution because certain phenotypes are fit, while others are universally unfit. Such constraints should be evident in traits that, across many species, cluster around particular modal values, with few intermediates between modes. Here, I compile a new global database of 599 species from 94 plant families showing that stomatal ratio, an important functional trait affecting photosynthesis, is multimodal, hinting at distinct peaks in the adaptive landscape. The dataset confirms that most plants have all their stomata on the lower leaf surface (hypostomy), but shows for the first time that species with roughly half their stomata on each leaf surface (amphistomy) form a distinct mode in the trait distribution. Based on a new evolutionary process model, this multimodal pattern is unlikely without constraint. Further, multimodality has evolved repeatedly across disparate families, evincing long-term constraint on the adaptive landscape. A simple cost-benefit model of stomatal ratio demonstrates that selection alone is sufficient to generate an adaptive landscape with multiple peaks. Finally, phylogenetic comparative methods indicate that life history evolution drives shifts between peaks. This implies that the adaptive benefit conferred by amphistomy – increased photosynthesis – is most important in plants with fast life histories, challenging existing ideas that amphistomy is an adaptation to thick leaves and open habitats. I conclude that peaks in the adaptive landscape have been constrained by selection over much of land plant evolution, leading to predictable, repeatable patterns of evolution.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".