Surprisingly weak coordination between leaf structure and function among closely-related tomato species
Bibliographic record
Abstract
Natural selection may often favor coordination between different traits, or phenotypic integration, in order to most efficiently acquire and deploy scarce resources. As leaves are the primary photosynthetic organ in plants, many have proposed that leaf physiology, biochemistry, and anatomical structure are coordinated along a functional trait spectrum from fast, resource-acquisitive syndromes to slow, resource-conservative syndromes. However, the coordination hypothesis has rarely been tested at a phylogenetic scale most relevant for understanding rapid adaptation in the recent past or predicting evolutionary trajectories in response to climate change. To that end, we used a common garden to examine genetically-based coordination between leaf traits across 19 wild and cultivated tomato taxa. We found surprisingly weak integration between photosynthetic rate, leaf structure, biochemical capacity, and CO2 diffusion, even though all were arrayed in the predicted direction along a 'fast-slow' spectrum. This suggests considerable scope for unique trait combinations to evolve in response to new environments or in crop breeding. In particular, we find that partially independent variation in stomatal and mesophyll conductance may allow a plant to improve water-use efficiency without necessarily sacrificing maximum photosynthetic rates. Our study does not imply that functional trait spectra or tradeoffs are unimportant, but that the many important axes of variation within a taxonomic group may be unique and not generalizable to other taxa.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".