Habitat associations of Rafinesque’s big-eared bats (<i>Corynorhinus rafinesquii</i>) and their lepidopteran prey in bottomland hardwood forests
Bibliographic record
Abstract
Effective conservation of forest bats requires knowledge of daytime and nocturnal habitat use. Although many bat species use different habitats during the daytime and nighttime hours, these data are rarely collected simultaneously. We radio-tagged 48 adult Rafinesque’s big-eared bats (Corynorhinus rafinesquii (Lesson, 1827)) in a bottomland hardwood forest in Kentucky from 2009 to 2011. We tracked bats to 64 day roosts over 549 bat-days and found bats roosted almost exclusively in trees located in forested wetlands (n = 59; 92%), and that reproductive females established their home ranges closest to these habitats (P < 0.0001). Although few (n = 4; 6%) roosts were located in deciduous forests on drier soils, these forests were important foraging habitats for pregnant females, which foraged closest to these habitats within their home ranges (P = 0.04). Abundance of Lepidoptera differed among habitats (P = 0.03), with higher abundance in deciduous forests and along forest–field edges. Deciduous forests were the only habitat preferentially selected by any lepidopteran family (Notodontidae), and the only habitat not avoided by any family. These data confirm the importance of forested wetlands to the ecology of Rafinesque’s big-eared bat and demonstrate the benefit of proximally located deciduous forests that provide habitat for their moth prey.
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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".