Data from: Molecular diet analysis finds an insectivorous desert bat community dominated by resource sharing despite diverse echolocation and foraging strategies
Bibliographic record
Abstract
Interspecific differences in traits can alter the relative niche use of species within the same environment. Bats provide an excellent model to study niche use because they have a wide variety of behavioural, acoustic and morphological traits that may lead to multi-species, functional groups. Predatory bats have been classified by their foraging location (edge, clutter, open space), ability to aerial hawk and/or substrate glean prey and echolocation call design and flexibility, all of which may dictate their diet. For example, high frequency, broadband calls do not travel far but offer high object resolution while high intensity, low frequency calls travel further but provide lower resolution. Because these behaviours can be flexible four behavioural categories have been proposed: (1) gleaning, (2) behaviourally flexible (gleaning and hawking), (3) clutter tolerant hawking, and (4) open space hawking. Recent studies of diet in bats use molecular tools to identify prey but mainly focus on one or two species in isolation and few studies provide evidence for substantial differences in prey use despite the many behavioural, acoustic and morphological differences. Here we analyse the diet of 17 sympatric species in the Chihuahuan desert and test the hypothesis that peak echolocation frequency and behavioural categories are linked to differences in diet. We find no significant correlation between dietary richness and echolocation frequency (though it spanned close to 100kHz across species). However, our data suggest that behaviourally flexible bats that use gleaning and aerial hawking have the broadest diets and are the most differentiated from clutter-tolerant aerial hawking 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.207 | 0.089 |
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".