Tropical phenology: bi‐annual rhythms and interannual variation in an Afrotropical butterfly assemblage
Bibliographic record
Abstract
Temporal variation and phenology of tropical insect communities and the role of environmental factors controlling this variation is poorly understood. A better understanding is needed, for example, to predict the effects of climate change on tropical insect communities and to assess the long‐term persistence of tropical communities. We studied seasonal and inter‐annual variation in tropical fruit‐feeding butterflies by exploiting a unique 137‐month abundance time series of >100 species, sampled at 22 locations in the medium altitude montane rain forest of Kibale National Park, western Uganda. Precipitation peaked twice per year, about 20 d after each equinox. Vegetation greenness peaked approximately 33 d later. Species richness and abundance of butterflies peaked about 2 and 3 months, respectively, after the greenness peak. Furthermore, temporal shifts in peaks of butterfly abundances of each 6‐month cycle positively correlated with temporal shifts in peaks of vegetation greenness approximately three months before. The butterfly assemblages of ENSO warm phase years differed significantly from assemblages of the other years. To our knowledge this is the first elucidation of bi‐annual rhythms in butterfly assemblages. Host plant availability could explain the seasonal cycles in butterfly abundance and species richness, because the 3‐month lag observed matches with the egg‐to‐adult development time in the studied species.
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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 | 0.000 | 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".