Direct and indirect effects of climate on richness drive the latitudinal diversity gradient in forest trees
Bibliographic record
Abstract
Climate is widely recognised as an important determinant of the latitudinal diversity gradient. However, most existing studies make no distinction between direct and indirect effects of climate, which substantially hinders our understanding of how climate constrains biodiversity globally. Using data from 35 large forest plots, we test hypothesised relationships amongst climate, topography, forest structural attributes (stem abundance, tree size variation and stand basal area) and tree species richness to better understand drivers of latitudinal tree diversity patterns. Climate influences tree richness both directly, with more species in warm, moist, aseasonal climates and indirectly, with more species at higher stem abundance. These results imply direct limitation of species diversity by climatic stress and more rapid (co-)evolution and narrower niche partitioning in warm climates. They also support the idea that increased numbers of individuals associated with high primary productivity are partitioned to support a greater number of species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".