A Global Regionalisation of Tree Functional Capacity
Bibliographic record
Abstract
ABSTRACT Aim Understanding the global distribution of tree functional diversity is essential for predicting ecosystem responses to environmental change. Traditional biogeographic regionalisations classify ecosystems based on species composition and climate but overlook functional traits, which directly govern processes such as carbon storage, nutrient cycling and productivity. Here, we present the first global functional regionalisation of tree‐inhabited systems into functionally distinct regions based on tree traits rather than taxonomic or climatic boundaries. Location Global, covering forests, savannas and grasslands. Time Period Present. Major Taxa Studied Trees. Methods We compiled over 5 million tree species occurrences and associated functional traits related to photosynthesis, growth, reproduction, structure and physiology. Using a stability‐based clustering approach, we identified functional macro‐regions (85% variance explained), meso‐regions (90%) and micro‐regions (99%). We also identified important traits discriminating these regions and assessed the relative influence of climate and phylogeny in shaping these functional boundaries. Results We identified five major functional macro‐regions: Boreal, Cool Temperate, Warm Temperate, Neotropical and Paleotropical. Boreal and temperate macro‐regions align closely with climatic zones, while tropical macro‐regions are structured primarily by evolutionary history rather than moisture availability. Functional differentiation is driven by photosynthetic and reproductive traits across scales, with structural, growth and physiological traits dominating at macro, meso and micro levels, respectively. Phylogenetic distance explains 68% of the functional divergence between Neotropical and Paleotropical macro‐regions, whereas environmental differences drive 42%–44% of the variance between tropical and temperate regions. Main Conclusions Our study provides one of the first global functional classifications of tree‐inhabited ecosystems, revealing that climate primarily structures temperate and boreal regions, while evolutionary history drives tropical functional diversity. These findings offer a new perspective on global biogeography and ecosystem resilience, with implications for biodiversity conservation and climate adaptation strategies.
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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 | 0.003 | 0.002 |
| 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) | 0.002 | 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".