What controls tropical forest architecture? Testing environmental, structural and floristic drivers
Bibliographic record
Abstract
Abstract Aim To test the extent to which the vertical structure of tropical forests is determined by environment, forest structure or biogeographical history. Location Pan‐tropical. Methods Using height and diameter data from 20,497 trees in 112 non‐contiguous plots, asymptotic maximum height ( H AM ) and height–diameter relationships were computed with nonlinear mixed effects ( NLME ) models to: (1) test for environmental and structural causes of differences among plots, and (2) test if there were continental differences once environment and structure were accounted for; persistence of differences may imply the importance of biogeography for vertical forest structure. NLME analyses for floristic subsets of data (only/excluding Fabaceae and only/excluding Dipterocarpaceae individuals) were used to examine whether family‐level patterns revealed biogeographical explanations of cross‐continental differences. Results H AM and allometry were significantly different amongst continents. H AM was greatest in A sian forests (58.3 ± 7.5 m, 95% CI ), followed by forests in A frica (45.1 ± 2.6 m), A merica (35.8 ± 6.0 m) and A ustralia (35.0 ± 7.4 m), and height–diameter relationships varied similarly; for a given diameter, stems were tallest in A sia, followed by A frica, A merica and A ustralia. Precipitation seasonality, basal area, stem density, solar radiation and wood density each explained some variation in allometry and H AM yet continental differences persisted even after these were accounted for. Analyses using floristic subsets showed that significant continental differences in H AM and allometry persisted in all cases. Main conclusions Tree allometry and maximum height are altered by environmental conditions, forest structure and wood density. Yet, even after accounting for these, tropical forest architecture varies significantly from continent to continent. The greater stature of tropical forests in A sia is not directly determined by the dominance of the family Dipterocarpaceae, as on average non‐dipterocarps are equally tall. We hypothesise that dominant large‐statured families create conditions in which only tall species can compete, thus perpetuating a forest dominated by tall individuals from diverse families.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".