A Globally Consistent Richness‐Climate Relationship for Angiosperms
Bibliographic record
Abstract
Species richness, the simplest index of biodiversity, varies greatly over broad spatial scales. Richness-climate relationships often account for >80% of the spatial variance in richness. However, it has been suggested that richness-climate relationships differ significantly among geographic regions and that there is no globally consistent relationship. This study investigated the global patterns of species and family richness of angiosperms in relation to climate. We found that models relating angiosperm richness to mean annual temperature, annual water deficit, and their interaction or models relating richness to annual potential evapotranspiration and water deficit are both globally consistent and very strong and are independent of the diverse evolutionary histories and functional assemblages of plants in different parts of the world. Thus, effects of other factors such as evolutionary history, postglacial dispersal, soil nutrients, topography, or other climatic variables either must be quite minor over broad scales (because there is little residual variation left to explain) or they must be strongly collinear with global patterns of climate. The correlations shown here must be predicted by any successful hypothesis of mechanisms controlling richness patterns.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".