Effect of habitat loss and fragmentation on fruit-feeding butterflies in the Brazilian Atlantic Forest
Bibliographic record
Abstract
Habitat loss and fragmentation have drastically altered the availability and quality of tropical forest habitats, but information on how such changes influence local biodiversity is still insufficient. Here, we examine the effects of both patch and landscape metrics on fruit-feeding butterfly assemblages in a fragmented landscape of the Brazilian Atlantic Forest. Our study was carried out in three habitat types: eight fragments (ranging from 8 to 126 ha), eight areas of forest edge (50 m from forest border), and eight areas of forest interior (>200 m from forest border) of the largest remnant (3500 ha) of the Atlantic Forest of northeast Brazil. Our results demonstrated that fragment area is negatively correlated with observed and estimated richness and abundance of butterflies, whereas habitat type is correlated with estimated richness and abundance of butterflies. Species composition responded to habitat type, fragment area, and distance between sample units. These findings illustrated (i) fruit-feeding butterfly sensitivity to habitat loss and fragmentation, (ii) that species composition and abundance are adequate parameters to access the responses of fruit-feeding butterflies to habitat loss and fragmentation, and (iii) the relevance of a heterogeneous and connected landscape for conservation of butterflies, where small fragments are important for generalist or open-habitat specialists and large remnants are key for disturbance-sensitive and threatened taxa.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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".