Scaling relationships among functional traits are similar across individuals, species, and communities
Bibliographic record
Abstract
Abstract Question Bivariate relationships among functional traits reflect how plants adjust to environments through the allocation of limiting resources. Bivariate relationships are well studied across species, but whether the nature of these trait relationships changes across organizational levels (individual, species, community), and whether processes driving these relationships vary across these levels, is seldom explored. Location The tropical cloud forests of the Bawangling Nature Reserve, Jianfengling Nature Reserve and Limushan Nature Reserve on Hainan Island, Southern China. Methods We measured leaf mass per area (LMA), plant height (H) and wood density (WD) for 4,748 individual trees, 174 species and 48 communities in three tropical cloud forests, and recorded five soil characteristics that are important for plant growth. We evaluated bivariate relationships between these traits across the three organizational levels, and assessed the effects of soil conditions on these trait relationships. Results LMA versus H, WD versus H, and LMA versus WD were all positively and disproportionately related, suggesting differential carbon investment between leaves and stem, as well as between stem height and stem density. The slopes of these relationships did not differ significantly across the three levels, suggesting a similar allocation strategy operating at different hierarchical levels. Soil phosphorus had a significant effect on the scaling exponents across all three organizational levels, indicating that phosphorus limitation in cloud forests is a principal driver of resource allocation patterns in trees. conclusions We conclude that tropical cloud forest trees have relatively consistent scaling relationships between three primary functional traits across the individual, species, and community levels. The coordinated resource allocation strategies in plants are most likely driven by the prevailing environmental constraints.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".