Butterfly species richness and community composition in forests affected by ENSO-induced burning and habitat isolation in Borneo
Bibliographic record
Abstract
Landscape-scale studies of community traits such as species richness and community composition are sorely needed to explore the impact of large-scale disturbance events such as ENSO (El Niño Southern Oscillation)-induced burning and habitat isolation on rain-forest communities. Here we assess butterflies in continuous forest, in unburned isolates surrounded by burned forest and in burned forest, in addition to areas sampled before the most recent (1997/98) large-scale burn event in Borneo. Overall levels of species richness were significantly higher pre-ENSO and in continuous forest than in unburned isolates and burned forest. There was, however, some variation among butterfly families in these patterns, with no significant differences among habitats (continuous forest, isolates and burned forest) for the Hesperiidae and significant differences for the other butterfly families. Patterns of community composition showed that similarity was greater between distant continuous forest and isolates than between either of these and burned forest. Since the unburned isolates were surrounded by the burned forest this indicates that the habitat (burned or unburned) overrides geographical differences. Dominant species that contributed substantially to differences among habitats were often completely absent from either burned or unburned forest. The combined patterns of species richness and community structure suggest that burning affects forest ecosystems by a replacement of dominant species while habitat isolation may affect areas by leading to the local extinction of rare 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 | 0.000 |
| 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".