GARD 1.5 range shapefiles used in: Global diversity patterns are explained by diversification rates at ancient, not shallow, timescales
Bibliographic record
Abstract
AbstractExplaining global species richness patterns is a “Holy Grail” of ecology and evolution. These richness patterns are often attributed to spatial variation in diversification rates (speciation minus extinction). Surprisingly, prominent studies of birds, fish, and angiosperms reported higher diversification rates at higher latitudes (mismatched with richness). Yet these studies only examined diversification rates at relatively recent timescales. Here, we quantify global richness patterns among lizard and snake species (10,213; 94%) and explore their underlying causes. We found that diversification rates at more recent timescales in squamates also show mismatched patterns of rates and richness. However, diversification rates at deeper timescales were positively related to richness. These observations may help resolve the paradoxical results of previous studies. Remarkably, these diversification patterns are largely unrelated to climate. Instead, higher tropical richness is related to ancient occupation of tropical regions. Thus, these large-scale diversity patterns are only understood by considering climate, deep-time diversification rates, and the time spent in different regions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.049 |
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".