Effects of Selective Logging on Bat Communities in the Southeastern Amazon
Bibliographic record
Abstract
Although extensive areas of tropical forest are selectively logged each year, the responses of bat communities to this form of disturbance have rarely been examined. Our objectives were to (1) compare bat abundance, species composition, and feeding guild structure between unlogged and low-intensity selectively logged (1-4 logged stems/ha) sampling grids in the southeastern Amazon and (2) examine correlations between logging-induced changes in bat communities and forest structure. We captured bats in understory and canopy mist nets set in five 1-ha study grids in both logged and unlogged forest. We captured 996 individuals, representing 5 families, 32 genera, and 49 species. Abundances of nectarivorous and frugivorous taxa (Glossophaginae, Lonchophyllinae, Stenodermatinae, and Carolliinae) were higher at logged sites, where canopy openness and understory foliage density were greatest. In contrast, insectivorous and omnivorous species (Emballonuridae, Mormoopidae, Phyllostominae, and Vespertilionidae) were more abundant in unlogged sites, where canopy foliage density and variability in the understory stratum were greatest. Multivariate analyses indicated that understory bat species composition differed strongly between logged and unlogged sites but provided little evidence of logging effects for the canopy fauna. Different responses among feeding guilds and taxonomic groups appeared to be related to foraging and echolocation strategies and to changes in canopy cover and understory foliage densities. Our results suggest that even low-intensity logging modifies habitat structure, leading to changes in bat species composition.
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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 | 0.000 | 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) | 0.000 | 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".