Macroevolutionary dynamics in the transition of angiosperms to aquatic environments
Bibliographic record
Abstract
Angiosperm lineages in aquatic environments are characterized by high structural and functional diversity, and wide distributions. A long-standing evolutionary riddle is what processes have caused the relatively low diversity of aquatic angiosperms compared to their terrestrial relatives. We use diversification and ancestral reconstruction models with a comprehensive > 10 000 genus angiosperm phylogeny to elucidate the macroevolutionary dynamics associated with transitions of terrestrial plants to water. Our study reveals that net diversification rates are significantly lower in aquatic than in terrestrial angiosperms due to lower speciation and higher extinction. Shifts from land to water started early in angiosperm evolution, but most events were concentrated during the last c. 25 million years. Reversals to a terrestrial habitat started only 40 million years ago, but occurred at much higher rates. Within aquatic angiosperms, the estimated pattern is one of gradual accumulation of lineages, and relatively low and constant diversification rates throughout the Cenozoic. Low diversification rates, together with infrequent water transitions, account for the low diversity of aquatic angiosperms today. The stressful conditions and small global surface of the aquatic habitat available for angiosperms are hypothesized to explain this pattern.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".