Trait‐dependent diversification in angiosperms: Patterns, models and data
Bibliographic record
Abstract
Variation in species richness across the tree of life, accompanied by the incredible variety of ecological and morphological characteristics found in nature, has inspired many studies to link traits with species diversification. Angiosperms are a highly diverse group that has fundamentally shaped life on earth since the Cretaceous, and illustrate how species diversification affects ecosystem functioning. Numerous traits and processes have been linked to differences in species richness within this group, but we know little about their relative importance and how they interact. Here, we synthesised data from 152 studies that used state-dependent speciation and extinction (SSE) models on angiosperm clades. Intrinsic traits related to reproduction and morphology were often linked to diversification but a set of universal drivers did not emerge as traits did not have consistent effects across clades. Importantly, SSE model results were correlated to data set properties - trees that were larger, older or less well-sampled tended to yield trait-dependent outcomes. We compared these properties to recommendations for SSE model use and provide a set of best practices to follow when designing studies and reporting results. Finally, we argue that SSE model inferences should be considered in a larger context incorporating species' ecology, demography and genetics.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".