Wood density explains architectural differentiation across 145 co‐occurring tropical tree species
Bibliographic record
Abstract
Summary 1.Because of its mechanical properties, wood density may affect the way that trees expand their stem and crown to exploit favourable light conditions in a mechanically stable way. From engineering theory and wood density properties, it is predicted that in terms of biomass investment, low‐density wood is more efficient for vertical stem expansion, while high‐density wood is more efficient for horizontal branch expansion. So far, these predictions have rarely been tested by empirical studies. 2.We tested these predictions for 145 co‐occurring tree species in a Malaysian tropical rainforest. For each species, we selected trees across a broad size range and measured architectural dimensions (stem diameter, height of the lowest foliage and crown width). We used a hierarchical Bayesian model to estimate species‐specific allometric relationships between architectural dimensions including estimated stem biomass. Then, we examined correlations between species wood density and estimated architectural variables at standardized heights. 3.When species were compared at standardized tree heights, wood density correlated negatively with stem diameter and positively with stem biomass at most reference heights. This indicates that species with low wood density produce thicker stems but at lower biomass costs. Wood density correlated positively with crown width and negatively with height of the lowest foliage, which indicates that high wood density species have wider and deeper crowns than low wood density species. These relationships were maintained at most reference heights. However, the relationship with crown width was nonsignificant above 18 m height. This may reflect large plastic response of lateral crown expansion to a local condition. 4. Wood density explains the trade‐off between effective vertical stem expansion and horizontal crown expansion across co‐occurring tropical tree species. Such mechanical constraints characterize the difference in tree architecture between low wood density species that show an efficient height expansion to attain better light conditions in the exposed canopy and high wood density species that show an efficient horizontal crown expansion to enhance current light interception and persistence in the shaded forest understorey. Our study thus suggests that the mechanical constraints set by wood density contribute to the co‐existence of species differing in architecture and light capture strategy.
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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".