Global Diversity Patterns are Explained by Diversification Rates and Dispersal at Ancient, Not Shallow, Timescales
Bibliographic record
Abstract
Explaining global species richness patterns is a major goal of evolution, ecology, and biogeography. These richness patterns are often attributed to spatial variation in diversification rates (speciation minus extinction). Surprisingly, prominent studies of birds, fish, and plants have reported higher speciation and/or diversification rates at higher latitudes, where species richness is lower. We hypothesize that these surprising findings are explained by the focus of those studies on relatively recent macroevolutionary rates, within the last ~20 million years. Here, we analyze global richness patterns among 10,213 squamates (lizards and snakes) and explore their underlying causes. We find that when diversification rates were quantified at more recent timescales, we observed mismatched patterns of rates and richness, similar to previous studies in other taxa. Importantly, diversification rates estimated over longer timescales were instead positively related to geographic richness patterns. These observations may help resolve the paradoxical results of previous studies in other taxa. We found that diversification rates were largely unrelated to climate, even though climate and richness were related. Instead, higher tropical richness was related to the ancient occupation of tropical regions, with colonization time the variable that explained the most variation in richness overall. We suggest that large-scale diversity patterns might be best understood by considering climate, deep-time diversification rates, and the time spent in different regions, rather than recent diversification rates alone.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".