Trait variability differs between leaf and wood tissues across ecological scales in subtropical forests
Bibliographic record
Abstract
Abstract Question Revealing how plant traits vary over disparate spatial scales and how ecological processes mediate such variation is important for understanding plant community assembly. However, to what extent does the distribution of trait variation among ecological scales differ between leaf and wood tissues and between physical and chemical traits? What are the consequences of resource competition and/or habitat filtering on the community assembly with respect to differences between leaf and wood traits, and between physical and chemical traits? Location Subtropical evergreen broad‐leaved forests in five sites in the Ningbo area (29°41–50′ N, 121°36–52′ E) in eastern China. Methods Traits of 96 woody plant species were sampled and variation of ten physical‐ and chemical‐based leaf and wood traits were partitioned across six ecological scales (site, plot, species, individual plant, twigs and leaf age) using a linear mixed model. Results From individual plant to site scales, variance partitions were distinct between leaf and wood traits. In leaf tissues, physical and chemical traits showed a consistent pattern, with the majority of variation found among species and individual plants, with little among plots. For wood tissues, the largest variation in physical traits was at the species and individual plant scales, with the largest variation in chemical traits observed at the plot scale. Variance partition was markedly similar within and across species. Conclusion Leaf and wood traits vary differently in relation to ecological scale, suggesting that trait variability is tissue‐specific. The large variability of wood traits at the plot scale suggests a strong habitat filtering process. The large variation in leaf traits within plots may reflect niche differentiation across species and the importance of intra‐specific variation that affects species co‐existence. Our study demonstrated that physical and chemical traits may be independent. These decoupled trait axes may increase the dimensionality of niche space and facilitate species co‐existence in forest communities.
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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 | 0.001 |
| 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".