Wing morphology of Neotropical bats: a quantitative and qualitative analysis with implications for habitat use
Bibliographic record
Abstract
Wing morphology has a direct influence on the flight manoeuvrability, agility, and speed of bats. Studies addressing the relationship between bat wing morphology and ecology are biased towards Old World species and few of them have addressed the ecologically rich Amazonian bat fauna. We quantitatively and qualitatively characterized the wing shape of 51 bat species found in the Brazilian Amazonia by measuring their aspect ratio (AR) and relative wing load (RWL). We found a high variability in wing shape: AR varied from 5.0862 (pygmy round-eared bat, Lophostoma brasiliense (Peters, 1866)) to 8.2774 (brown dog-faced bat, Molossus (Cynomops) paranus (Thomas, 1901)), while RWL varied from 20.0459 (spectral bat, Vampyrum spectrum (L., 1758)) to 55.3931 (Pallas’s mastiff, Molossus molossus (Pallas, 1766)). Insectivores had the largest variability, whereas frugivores and nectarivores had intermediate values with lower variability, indicating a higher flexibility in the use of space and resources. Our predictions on flight patterns are supported by capture and behavioural data from the literature, both of which point to the use of wing shape as a good proxy for habitat use and food partitioning among species. Our data are useful for integrative studies in ecology, physiology, behaviour, and evolution, and can contribute to a better understanding of the ecological interactions of Neotropical bat species.
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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 | 0.001 | 0.002 |
| 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".