Variation in diet of frugivorous bats in fragments of Brazil’s Atlantic Forest associated with vegetation density
Bibliographic record
Abstract
Abstract Species distribution and persistence have long been known to vary with landscape structure; however, continued human activities in altered landscapes raise many questions as to how habitat fragmentation impacts the biology of persistent animal populations. Using carbon and nitrogen stable isotope analysis, we examined interspecific variation in the diet of frugivorous bats among remnant habitat patches of Brazil’s Atlantic Forest. We hypothesized that the diet of individuals captured in habitat patches would be different than those captured in contiguous habitats. We predicted that bats would alter their realized dietary niche breadth, taking food items (i.e., fruits or insects) according to landscape structure. However, more mobile species should be less impacted by small-scale landscape changes. We predicted that (1) a wide-ranging species (Artibeus lituratus), which move through open areas, will be less affected by small-scale landscape attributes, patch size, composition, and isolation; while (2) two narrow-ranging species (Carollia perspicillata and Sturnira lilium) will have more variation between populations in niche breadth and isotopic ratio ranges dependant on the local environment. Using Akaike’s Information Criterion (AIC) to rank a priori selected candidate models to explain variation, we found that fragment composition, largely involving vegetation density rather than spatial aspects of landscape structure (i.e., patch area, isolation) best explained diet variation in frugivorous bats. Additionally, there was evidence that wide-ranging A. lituratus were less impacted by differences in the landscape than narrow-ranging species. This supports the prediction that bats resident to fragments have altered feeding behavior, in response to environmental perturbation.
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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 | 0.001 |
| 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".