Global trade-offs in tree functional traits
Bibliographic record
Abstract
A bstract Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. When considering trait relationships across the entire plant kingdom, plant trait frameworks typically must omit traits unique to large woody species, thereby limiting our understanding of how these distinct ecological pressures shape trait relationships in trees. Here, by considering 18 functional traits—reflecting leaf economics, wood structure, tree size, reproduction, and below-ground allocation—we quantify the major axes of variation governing trait expression of trees worldwide. We show that trait variation within and across angiosperms and gymnosperms is captured by two independent processes: one reflecting tree size and competition for light, the other reflecting leaf photosynthetic capacity and nutrient economies. By exploring multidimensional relationships across clusters of traits, we further identify a representative set of seven traits which captures the majority of variation in form and function in trees: maximum tree height, stem conduit diameter, specific leaf area, seed mass, bark thickness, root depth, and wood density. Collectively, this work informs future trait-based research into the functional biogeography of trees, and contributes to our fundamental understanding of the ecological and evolutionary controls on forest biodiversity and productivity worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".