Bat Mobility and Roosts in a Fragmented Landscape in Central Amazonia, Brazil1
Bibliographic record
Abstract
In spite of the important role played by bats in tropical ecosystems, little is known about how they are affected by habitat fragmentation. By using a mark/recapture protocol and radiotelemetry techniques in a naturally fragmented landscape composed of primary forests and forest fragments surrounded by savannas in Alter do Chão, Pará State, Brazil, we were able to track the movements of various species of bats, calculate the size of the area used, locate roosts and potential feeding areas, and determine preferred flight routes. We marked 3440 bats belonging to 44 species and recaptured 151 belonging to 14 species. The average distance between extra-site recaptures was 2.2 km. With the exception of bats marked in fragments and recaptured in forests, all other possible inter-habitat recaptures were observed. We selected 23 bats of 8 species for radiotelemetry and the areas used by them varied from 65 to 530 ha. Some species restricted their activity to the vicinity of their roosts, rarely moving more than 500 m away, but others traveled greater distances between roosts and foraging areas. All tracked bats flew over savannas, crossing distances from 0.5 to 2.5 km. Roost location and type varied among species, from individuals roosting alone in the foliage to colonies in buildings. Bats were highly mobile and savannas did not appear to inhibit the movements of some species, suggesting that a persistent biological flow may be maintained among isolated fragments, with bats acting as pollinators and seed dispersers.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".