Leaf Trait Covariation and Its Controls: A Quantitative Data Analysis Along a Subtropical Elevation Gradient
Bibliographic record
Abstract
Abstract Elevation gradients are frequently treated as useful space‐for‐time substitutions for inferring trait variations in response to different environmental conditions. The independent variations in leaf traits in response to elevation are well understood, but far less is known about trait covariation and its controls. This limits our understanding of the principles and mechanisms of leaf trait covariation, especially along elevation gradients in subtropical forests. Here, we studied the covariation among seven functional traits, including leaf size (LS), leaf nitrogen per unit mass (Nmass), leaf nitrogen per unit area (Narea), leaf mass per area (LMA), leaf dry matter content (LDMC), leaf thickness (LT) and the leaf internal‐to‐ambient CO2 ratio (Ci:Ca, termed χ). Sampling was conducted on 41 species in a subtropical forest on Mount Huangshan, China, and the data were analyzed using multivariate analysis and variance partitioning procedures. We found that (a) The first three principal components captured 79% of the total leaf trait covariation, which was caused mainly by within site differences; (b) Nmass and LDMC were positively correlated with soil water content (SW) and negatively correlated with vapor pressure deficit (VPD), while χ showed negative relationships with elevation; and (c) 78% of the variation in the studied plant functional traits could be explained by climate, soil, and family controls in combination, while family distribution was the most important determining factor for trait covariation along the elevation gradient. Our findings provide relevant insights into plant adaptation to environmental gradients and present useful guidelines for ecosystem management and planning.
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 | 0.002 |
| 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".