Burning and logging differentially affect endemic vs. widely distributed butterfly species in Borneo
Bibliographic record
Abstract
ABSTRACT We assess the differential impact of logging and ENSO (El Niño Southern Oscillation)‐induced disturbance on the relative butterfly abundance and species richness of range‐restricted and widespread species within the island of Borneo. Relative abundance and species richness were assessed using rarefaction and species accumulation curves in unburned isolates surrounded by burned forest, the burned forest itself, and continuous forest unaffected by ENSO‐induced disturbance in addition to logged and unlogged landscapes in unburned forest. The relative abundance of endemics was significantly higher in unlogged forest than logged forest and significantly higher in unburned forest than burned forest. Rarefied species richness of range categories was similar (Bornean endemics) or higher (other categories) in selectively logged than unlogged forest. In contrast, rarefied species richness of range‐restricted species was highest in continuous forest, intermediate in unburned isolates, and lowest in burned forest. Only two individuals of a single Bornean endemic species were found in all the burned forest. Although species richness was higher in all range categories in continuous forest than in unburned isolates and in burned forest, the difference was most pronounced for range‐restricted species. Logging and ENSO‐induced fires thus have contrasting effects on range‐restricted species. While both increase the relative abundance of widely distributed species at the expense of range‐restricted species, only ENSO‐induced disturbance lowers the rarefied number of restricted range species. Our research highlights the threat that severe ENSO events pose to geographically restricted classes of biodiversity.
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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 | 0.000 |
| 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".