Rainfall variability and deciduous-evergreen coexistence in tropical forests
Bibliographic record
Abstract
Abstract In tropical forests, deciduous and evergreen leaf habits represent contrasting tree adaptations to precipitation seasonality. Both rainfall seasonality and interannual variation in rainfall are determinants of forest deciduousness, but their relative influence is not well understood. In this study, we evaluate the extent of deciduous-evergreen coexistence in tropical forests and develop a simple model of competition for water between leaf habits. Using this model, we formalize two mechanisms representing rainfall variability across time scales that may explain their stable coexistence: the temporal storage effect via interannual variability in rainfall vs. rainfall partitioning via evergreen access to dry-season rainfall. In our model, both mechanisms resulted in coexistence, but coexistence was more robust via resource partitioning. Empirically, remotely sensed deciduousness increased with precipitation seasonality, but effects of interannual rainfall variability on deciduousness were minor. We hypothesize that dry-season rainfall may prove a stronger influence on coexistence between leaf habits, and that changes in rainfall seasonality will have a greater impact on forest deciduousness than changes in the interannual variability of rainfall.
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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 | 0.002 |
| 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 | 0.000 |
| 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".