Climatic drivers of dipterocarp mass-flowering in South-East Asia
Bibliographic record
Abstract
Abstract Dipterocarpaceae, a dominant family of trees in South-East Asian tropical forests, are remarkable in that they exhibit supra-annual mass-flowering events. The flowering patterns are related to the El Niño Southern Oscillation, but the mechanism that precipitates mass-flowering is still debated. Here, we test if a cumulative-trigger model that tracks resource availability, specifically light, may better explain dipterocarp phenology than a direct-environmental-trigger mechanism. Using 11 flowering time series with an average length of 29 y and variety of candidate predictor variables (precipitation, cloud cover, minimum temperature and El Niño indices) we could not find a plausible direct-environmental-trigger (median AUCs across regions from 0.53 to 0.57 indicating near random predictions). The cumulative-trigger model based on El Niño indices showed better predictive results (AUC 0.67), which could further be improved by resetting the resource at known flowering events (AUC 0.76). Additional support for a cumulative-trigger model comes from the observation that regional differences in the time of year of peak flowering correspond to where El Niño effects are strongest. We conclude that cumulative resource tracking is an evolutionary plausible trigger mechanism that has other primary evolutionary advantages, such as predator satiation.
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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 | 0.001 |
| 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 | 0.000 | 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".