Assessing the causes of diversification slowdowns: temperature‐dependent and diversity‐dependent models receive equivalent support
Bibliographic record
Abstract
Diversification rates vary over time, yet the factors driving these variations remain unclear. Temporal declines in speciation rates have often been interpreted as the effect of ecological limits, competition, and diversity dependence, emphasising the role of biotic factors. Abiotic factors, such as climate change, are also supposed to have affected diversification rates over geological time scales, yet direct tests of these presumed effects have mainly been limited to few clades well represented in the fossil record. If warmer climatic periods have sustained faster speciation, this could explain slowdowns in speciation during the Cenozoic climate cooling. Here, we apply state-of-the art diversity-dependent and temperature-dependent phylogenetic models of diversification to 218 tetrapod families, along with constant rate and time-dependent models. We confirm the prevalence of diversification slowdowns, and find as much support for temperature-dependent than diversity-dependent models. These results call for a better integration of these two processes in studies of diversification dynamics.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".