Temporal variation in abundance and diversity of butterflies in Bornean rain forests: opposite impacts of logging recorded in different seasons
Bibliographic record
Abstract
We used traps baited with fruit to examine how the temporal variation of butterflies within primary forest in Sabah, Borneo differed between species. In addition, we compared patterns of temporal variation in primary and selectively logged forest, and we tested the hypothesis that selective logging has different recorded impacts on species diversity of adults during the wet monsoon period and the drier remaining half of the year. Species of Satyrinae and Morphinae had significantly less-restricted flight periods than did species of Nymphalinae and Charaxinae, which were sampled mainly during the drier season, especially in primary forest. Species diversity of adults was significantly higher during the drier season in primary forest, but did not differ between seasons in logged forest. As a consequence, logging had opposite recorded impacts on diversity during wetter and drier seasons: primary forest had significantly higher diversity than logged forest during the drier season but significantly lower diversity than logged forest during the wetter monsoon season. The results of this study have important implications for the assessment of biodiversity in tropical rain forests, particularly in relation to habitat disturbance: short-term assessments that do not take account of seasonal variation in abundance are likely to produce misleading results, even in regions where the seasonal variation in rainfall is not that great.
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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 | 0.000 |
| 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".