Data from: Phylogenetic history of vascular plant metabolism revealed using a macroevolutionary common garden
Bibliographic record
Abstract
While the fundamental biophysics of C3 photosynthesis is highly conserved across plants, substantial variation in leaf structure and enzymatic activity translates into variability in rates of photosynthesis. Although this variation is well-documented, it remains poorly understood how photosynthetic rates evolve over short and long time scales, and whether these macroevolutionary changes are related to the evolution of key morphological and biochemical leaf traits. Large-scale comparative studies have been hampered by the substantial logistical and statistical challenges in disentangling evolutionary adaptation from environmental acclimation. Here we get around this limitation with a ‘macroevolutionary common garden’ approach in which we measured the metabolic traits Jmax and Vcmax from 111 phylogenetically diverse species in a shared environment. Using several phylogenetic comparative methods, we find substantial phylogenetic signal in these traits at shallow phylogenetic scales, but this signal dissipates quickly at deeper time scales. Leaf morphological traits exhibit phylogenetic signal over much deeper time scales, suggesting that these traits are less evolutionarily constrained than metabolic traits. Furthermore, we find that while morphological and biochemical traits (LMA, Narea and Carea) are weakly predictive of Jmax and Vcmax, evolutionary changes in these traits are mostly decoupled from changes in metabolic traits. This lack of tight evolutionary coupling implies that it may not be possible to use changes in these functional traits in response to global change to infer that photosynthetic strategy is also evolving.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.019 |
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".