Relating leaf traits to seedling performance in a tropical forest: building a hierarchical functional framework
Bibliographic record
Abstract
Trait-based approaches have been extensively used in community ecology to provide a mechanistic understanding of the drivers of community assembly. However, a foundational assumption of the trait framework, traits relate to performance, has been mainly examined through univariate relationships that simplify the complex phenotypic integration of organisms. We evaluate a conceptual framework in which traits are organized hierarchically combining trait information at the individual- and species-level from biomass allocation and organ-level traits. We focus on photosynthetic traits and predict that the positive effects of increasing plant leaf mass on growth depend on species-level leaf traits. We modeled growth data on more than 1,500 seedlings from 97 seedling species from a tropical forest in China. We found that seedling growth increases with allocation to leaves (high leaf area ratio and leaf mass fraction) and this effect is accentuated for species with high specific leaf area and leaf area. Also, we found that light has a significant effect on growth, and this effect is additive with leaf allocation traits. Our work offers an approach to gain further understanding of the effects of traits on the whole plant-level growth via a hierarchical framework including organ-level and biomass allocation traits at species and individual levels.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".