The global spectrum of tree crown architecture
Bibliographic record
Abstract
SUMMARY Trees can differ enormously in their crown architectural traits, such as the scaling relationships that link their height and crown size to their stem diameter. Yet despite the importance of crown architecture in shaping the structure and function of woody ecosystems, we lack a complete picture of what drives this incredible diversity in crown shapes. Using data from >500,000 globally distributed trees, we explored how climate, disturbance, competition, functional traits, and evolutionary history constrain the height, crown size and shape of the world’s tree species. We found that variation in height scaling relationships was primarily controlled by water availability and light competition. Conversely, crown width was predominantly shaped by exposure to wind and fire, while also covarying with other functional traits related to mechanical stability and photosynthesis. Additionally, several plant lineages had crown architectures that defy their environments, such as the exceedingly slender dipterocarps of Southeast Asia, or the extremely wide crowns of legumes in African savannas. Our study charts the global spectrum of tree crown architectural types. It provides a roadmap for integrating crown architecture with vegetation models and remote sensing observations, so that we may better understand the processes that shape the 3D structure of woody ecosystems.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".