Age‐related Crown Thinning in Tropical Forest Trees
Bibliographic record
Abstract
Abstract Gap dynamics theory proposes that treefall gaps provide high light levels needed for regeneration in the understory, and by increasing heterogeneity in the light environment allow light‐demanding tree species to persist in the community. Recent studies have demonstrated age‐related declines in leaf area index of individual temperate trees, highlighting a mechanism for gradual changes in the forest canopy that may also be an important, but less obvious, driver of forest dynamics. We assessed the prevalence of age‐related crown thinning among 12 tropical canopy tree species sampled in lowland forests in Panama and Puerto Rico (totalN = 881). Canopy gap fraction of individual canopy tree crowns was positively related to stem diameter at 1.3 m (diameter at breast height) in a pooled analysis, with 10 of 12 species showing a positive trend. Considered individually, a positive correlation between stem diameter and canopy gap fraction was statistically significant in 4 of 12 species, all of which were large‐statured canopy to emergent species:Beilschmiedia pendula, Ceiba pentandra, Jacaranda copaia,andPrioria copaifera. Pooled analyses also showed a negative relationship between liana abundance and canopy gap fraction, suggesting that lianas could be partially obscuring age‐related crown thinning. We conclude that age‐related crown thinning occurs in tropical forests, and could thus influence patterns of tree regeneration and tropical forest community dynamics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".