Functional and phylogenetic dimensions of tree biodiversity reveal unique geographic patterns
Bibliographic record
Abstract
Abstract Aim Quantify tree functional and phylogenetic richness and divergence at the global scale, and explore the drivers underpinning these biogeographic patterns. Location Global. Time Period Present. Major Taxa Studied Trees. Methods Using global tree occurrence data, we outlined species' observed ranges using individual alpha hulls to obtain per‐pixel tree species composition at a 0.83‐degree resolution. Using eight traits from a recent tree‐trait database and a vascular‐plant phylogeny we computed and mapped four pixel‐level biodiversity indices, including two metrics related to richness: phylogenetic richness and functional richness, and two related to divergence: mean pairwise phylogenetic distance and Rao's quadratic entropy. To account for the effect of species richness, we also calculated standardized effect sizes accounting for richness for each pixel. We then explored the relations between richness and divergence and the latitudinal patterns of divergence both globally and across biomes. Finally, we used a random forest modelling approach to test for drivers of the different dimensions of diversity in trees. Results In contrast to the latitudinal gradient in species richness, functional and phylogenetic divergence both peak in mid‐latitude systems, exhibiting the highest values in temperate ecosystems and lowest values in boreal and tropical forests. This result holds for functional divergence when removing gymnosperms but the peak flattens for phylogenetic divergence. Phylogenetic richness is consistently lower than expected given the number of species, whereas functional richness has higher‐than‐expected values at mid‐latitudes, mimicking functional divergence patterns. When considering the drivers of these diversity patterns, temperature and historical speciation rates consistently emerge as the strongest forces driving divergence, with negligible effects of human influence, soils or historical climate stability. Main Conclusions Collectively, these results reveal unique similarities and disparities across biomes that are not apparent in any single dimension of biodiversity, highlighting the importance of considering multiple aspects of biodiversity in the management of natural 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 | 0.000 |
| 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".