MATCHING MORPHOLOGY AND DIET IN THE DISC-WINGED BAT THYROPTERA TRICOLOR (CHIROPTERA)
Bibliographic record
Abstract
The dietary niche and morphological adaptations of a species should be highly correlated. However, conflicting selective pressures may make predictions about diet difficult without additional knowledge of a species' life history. We tested the reliability of predicting a bat's diet from its wing morphology using data for Spix's disk-winged bat (Thyroptera tricolor). The species had been predicted to fall within either the aerial hawking or gleaning foraging group. We compared the results of a theoretical (canonical discriminant function analysis of morphology) and an applied (analysis of droppings) method of diet determination. Our results place T. tricolor in the gleaning functional group with a 77% probability according to morphology. Correspondingly, a large proportion of the diverse diet consisted of nonflying prey, such as spiders, insect larvae, and other silent prey, which should be difficult to detect using echolocation. Although some flying prey were taken, it is clear that T. tricolor regularly gleans prey from surfaces, indicating that for this species, morphology is a useful indicator of diet. However, the breadth of the diet; the high proportion of jumping spiders, leafhoppers, and insect larvae; and the extremely small size of prey were unique features of the diet that could not be predicted from morphology alone. Thus, although comparative statistical methods and the analysis of wing morphology may be helpful to predict the general ecological niche, only detailed investigation of the life history may yield the detail needed for understanding the link between morphology and ecology of individual 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 | 0.000 | 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".