Solar radiation and <scp>ENSO</scp> predict fruiting phenology patterns in a 15‐year record from Kibale National Park, Uganda
Bibliographic record
Abstract
Abstract Fruiting, flowering, and leaf set patterns influence many aspects of tropical forest communities, but there are few long‐term studies examining potential drivers of these patterns, particularly in Africa. We evaluated a 15‐year dataset of tree phenology in Kibale National Park, Uganda, to identify abiotic predictors of fruit phenological patterns and discuss our findings in light of climate change. We quantified fruiting for 326 trees from 43 species and evaluated these patterns in relation to solar radiance, rainfall, and monthly temperature. We used time‐lagged variables based on seasonality in linear regression models to assess the effect of abiotic variables on the proportion of fruiting trees. Annual fruiting varied over 3.8‐fold, and inter‐annual variation in fruiting is associated with the extent of fruiting in the peak period, not variation in time of fruit set. While temperature and rainfall showed positive effects on fruiting, solar radiance in the two‐year period encompassing a given year and the previous year was the strongest predictor of fruiting. As solar irradiance was the strongest predictor of fruiting, the projected increase in rainfall associated with climate change, and coincident increase in cloud cover suggest that climate change will lead to a decrease in fruiting. ENSO in the prior 24‐month period was also significantly associated with annual ripe fruit production, and ENSO is also affected by climate change. Predicting changes in phenology demands understanding inter‐annual variation in fruit dynamics in light of potential abiotic drivers, patterns that will only emerge with long‐term data.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".