Trait coordination reveals the fast–slow plant economics spectrum along the vertical canopy profile in central Amazonian forests
Bibliographic record
Abstract
Abstract Understanding how environmental drivers affect tree functioning is essential to improve predictions of tropical forests' response to climate change. While functional traits directly influence tree performance, our understanding of how canopy environments shape their coordination and variation along the vertical forest profile remains limited. We quantified annual growth rates in terms of above‐ground biomass (AGB), the maximum efficiency of photosystem II (Fv/Fm) and six tree functional traits related to water transport (xylem density and Huber value), leaf morphology (leaf size, angle and stomatal density) and photosynthesis (specific leaf area) along the vertical forest profile in an old‐growth central Amazonian forest. To investigate the influence of canopy environments and ontogenetic stages on the variation of these traits, we divided the forest into three vertical strata defined by height from the ground (S1: 0–20 m; S2: 20–40 m; S3: >40 m). We sampled 162 branches and 486 leaves from 54 trees of 10 species, encompassing at least five of the most abundant species per stratum. Path analysis and correlation matrices were used to explore the links between canopy environments, traits and the ‘fast–slow’ plant economics spectrum. We found significant effects of height on relative tree growth, leaf size and specific leaf area. Trait correlations varied across strata suggesting an ecological stratification of canopy functional niches. Trait–growth correlations increased in number and strength with increasing height, suggesting greater trait‐mediated growth control in large trees. Our results reveal how traits and strategies on the ‘fast–slow’ plant economics spectrum are vertically distributed and coordinated along the forest profile. Our findings highlight important interactions between species and canopy environments in determining plant traits, with emergent species showing adaptive strategies at different stages of their development. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.000 | 0.000 |
| 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".