Species diversity of bats (Mammalia: Chiroptera) in forest fragments, primary forests, and savannas in central Amazonia, Brazil
Bibliographic record
Abstract
The exact number of species of bats in Brazilian Amazonia is not precisely known because relatively few sites have been surveyed in detail. Here we present an updated species list of the bats of Alter do Chão at the delta of the Tapajós River in Pará State. Using mist nets and acoustic monitoring of echolocation calls we systematically surveyed 5 forest sites, 14 natural forest fragments, and 12 savanna sites. We captured 3978 bats representing 70 species, 40 genera, and 7 families. Fifty species were recorded in savannas, 44 in forest sites, and 41 in forest fragments. The mean capture rate was higher in savanna and forest sites (0.747 and 0.741 bats/mist-net-hour (mnh), respectively) than in forest fragments (0.483 bats/mnh). Our list includes new records for Brazil and extends knowledge of the distribution of some species. Species-accumulation curves and species-richness estimators indicate that 75100 bat species occur at Alter do Chão, suggesting that our inventory recorded approximately 6789% of the bat fauna there. Using cluster analysis we compared the bat fauna at Alter do Chão with the faunas from 17 other sites in the Neotropics. There was 65% similarity with the fauna from Manaus (Brazil), 60% with that from Iwokrama (Guyana), and 57% with that from Paracou (Franch Guiana). Aspects of the conservation status of some species present at Alter do Chão are discussed.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".