Size-dependent changes in wood chemical traits: a comparison of neotropical saplings and large trees
Bibliographic record
Abstract
Wood anatomical traits are important correlates of life-history strategies among tree species, yet little is known about wood chemical traits. Additionally, size-dependent changes in wood chemical traits have been rarely examined, although these changes may represent an important aspect of tree ontogeny. Owing to selection for pathogen resistance and biomechanical stability, we predicted that saplings would show higher lignin (L) and wood carbon (Cconv), and lower holocellulose (H) concentrations, compared with conspecific large trees. To test these expectations, we quantified H, L and Cconv in co-occurring Panamanian tree species at the large tree vs. sapling size classes. We also examined inter- and intraspecific patterns using multivariate and phylogenetic analyses. In 15 of 16 species, sapling L concentration was higher than that in conspecific large trees, and in all 16 species, sapling H was lower than that in conspecific large trees. In 16 of 24 species, Cconv was higher in saplings than conspecific large trees. All large-tree traits were unrelated to sapling values and were unrelated to four life-history variables. Wood chemical traits did not show a phylogenetic signal in saplings, instead showing similar values across distantly related taxa; in large trees, only H showed a significant phylogenetic signal. Size-dependent changes in wood chemistry show consistent and predictable patterns, suggesting that ontogenetic changes in wood chemical traits are an important aspect of tree functional biology. Our results are consistent with the hypothesis that at early ontogenetic stages, trees are selected for greater L to defend against cellulose-decaying pathogens, or possibly to confer biomechanical stability.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".