Functional and taxonomic responses of tropical moth communities to deforestation
Bibliographic record
Abstract
Abstract Global insect decline has recently become a cause for major concern, particularly in the tropics where the vast majority of species occurs. Deforestation is suggested as being a major driver of this decline, but how anthropogenic changes in landscape structure affect tropical insect communities has rarely been addressed. We sampled Saturniidae and Sphingidae moths on 27 farms located in Brazilian Amazonia (Pará state) and characterised by different deforestation histories. We used functional traits (forewing length, body mass, wing load, trophic niche breadth and resource use strategy), analysed by combining RLQ and null model analyses, to investigate the responses of their taxonomic and functional diversity to landscape change dynamics and current structure. We found that communities had a higher proportion of large and polyphagous species with low wing load in landscapes with low forest quality and relative cover and high land use turnover. This was mainly due to a significant response to deforestation by saturniids, whereas the more mobile sphingids showed no significant landscape‐related pattern. We also observed an overall increase of species richness and functional dispersion in landscapes that have been deforested for a long time when compared with more recent agricultural settlements. Our results highlight the complex way in which landscape structure and historical dynamics interact to shape Neotropical moth communities and that saturniid moths respond clearly to the structure of the surrounding landscape, confirming their potential use as an indicator group for environmental monitoring programmes.
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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".