An exceptional role for flowering plant physiology in the expansion of tropical rainforests and biodiversity
Bibliographic record
Abstract
Movement of water from soil to atmosphere by plant transpiration can feed precipitation, but is limited by the hydraulic capacities of plants, which have not been uniform through time. The flowering plants that dominate modern vegetation possess transpiration capacities that are dramatically higher than any other plants, living or extinct. Transpiration operates at the level of the leaf, however, and how the impact of this physiological revolution scales up to the landscape and larger environment remains unclear. Here, climate modelling demonstrates that angiosperms help ensure aseasonally high levels of precipitation in the modern tropics. Most strikingly, replacement of angiosperm with non-angiosperm vegetation would result in a hotter, drier and more seasonal Amazon basin, decreasing the overall area of ever-wet rainforest by 80 per cent. Thus, flowering plant ecological dominance has strongly altered climate and the global hydrological cycle. Because tropical biodiversity is closely tied to precipitation and rainforest area, angiosperm climate modification may have promoted diversification of the angiosperms themselves, as well as radiations of diverse vertebrate and invertebrate animal lineages and of epiphytic plants. Their exceptional potential for environmental modification may have contributed to divergent responses to similar climates and global perturbations, like mass extinctions, before and after angiosperm evolution.
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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 | 0.000 | 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 | 0.001 |
| 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".