An inordinate fondness for beetles? Variation in seasonal dietary preferences of night‐roosting big brown bats (<i>Eptesicus fuscus</i>)
Bibliographic record
Abstract
Generalist species with numerous food web interactions are thought to provide stability to ecosystem dynamics; however, it is not always clear whether habitat generality translates into dietary diversity. Big brown bats are common across North America and employ a flexible foraging strategy over water, dense forests, forest edges and rural and urban settings. Despite this generalist use of habitat, they are paradoxically characterized as beetle specialists. However, hard carapaces may preferentially survive digestion leading to over-representation during morphological analysis of diet. This specialization has not been evaluated independently using molecular analysis and species-level identification of prey. We used next-generation sequencing to assess the diet of big brown bats. Beetles were consumed in the highest frequency but Lepidoptera species richness was highest among identified prey. The consumption of species showed strong seasonal and annual variation. While Coleoptera consumption varied, Lepidoptera and Ephemeroptera were relatively constant dietary components. Dietary diversity increased in late summer when insect diversity decreases. Our results indicate that big brown bats are dietary generalists and, while beetles are an important component of the diet, Lepidoptera are equally important, and Lepidoptera and Ephemeroptera are the only stable prey resource exploited. As resources become limited, big brown bats may respond by increasing the species richness of prey and thus their connectedness in the ecosystem. This characterization of diet corresponds well with a generalist approach to foraging, making them an important species in encouraging and maintaining ecosystem stability.
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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) | 0.002 | 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".