Eating local: influences of habitat on the diet of little brown bats (Myotis lucifugus)
Bibliographic record
Abstract
We employ molecular methods to profile the diet of the little brown bat, Myotis lucifugus, and describe spatial and temporal changes in diet over their maternity season. We identified 61 prey species of insects and 5 species of arachnid. The largest proportion of prey (∼32%) were identified as species of the mass-emerging Ephemeroptera (mayfly) genus Caenis. Bats roosting in agricultural settings had lower dietary richness than those occupying a roost located on a forest fragment in a conservation area. We detected temporal fluctuations in diet over the maternity season. Dipteran (fly) species dominated the diet early in the season, replaced later by species of mayfly. Because our methodology provides species-level identification of prey, we were able to isolate environmental indicator species in the diet and draw conclusions about the location and type of their foraging habitat and the health of these aquatic systems. The species detected suggested that the bats use variable habitats; members of one agricultural roost foraged on insects originating in rivers or streams while those in another agricultural roost and the forest roost fed on insects from pond or lake environments. All source water for prey was of fair to good quality, though no species detected are intolerant of pollution thus the habitat cannot be classified as pristine. Our study outlines a model system to investigate the abiotic and biotic interactions between habitat factors through this simple food chain to the top predator.
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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 | 0.000 |
| 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) | 0.001 | 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".