Global patterns of leaf construction traits and their covariation along climate and soil environmental gradients
Bibliographic record
Abstract
Summary Leaf functional traits and their covariation underlie plant ecological adaptations along environmental gradients, but there is limited information on the global covariation patterns of key leaf construction traits. To explore how leaf construction traits co‐vary across diverse climate and soil environmental conditions, we compiled a global dataset including cell wall mass per unit leaf mass (CW mass ), leaf carbon (C) and calcium (Ca) concentrations, and specific leaf area (SLA) for 2348 angiosperm species from 340 sites world‐wide. Our results demonstrated negative correlations between leaf C and Ca concentrations and between leaf C and SLA across diverse nongraminoid angiosperms. Leaf C concentration increased with increasing mean annual temperature (MAT) and mean annual precipitation (MAP) and with decreasing soil pH and calcium carbonate (CaCO 3 ) concentration, whereas leaf Ca concentration and SLA exhibited the opposite responses to these environmental variables. The covariations of leaf Ca–C and of leaf SLA–C were stronger in habitats with lower MAT and MAP, and/or higher soil CaCO 3 content. This global‐scale analysis demonstrates that the leaf C and Ca concentrations and SLA together govern the C and biomass investment strategies in leaves of nongraminoids. We conclude that environmental conditions strongly shape leaf construction traits and their covariation patterns.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".