The depth of edge influence among insectivorous bats at forest–field interfaces
Bibliographic record
Abstract
Species-specific variations in wing morphology and echolocation call characteristics often define which of three structural habitat types (open, cluttered, and edge) different bat species most frequently and efficiently use for foraging. Although edges are recognized as important habitats for commuting and foraging bats, no study to date has examined the depth of edge influence (DEI), the extent of quantitative changes in activity with distance from an edge, for any bat species. We focused our study on five species: northern long-eared bat, Myotis septentrionalis (Trouessart, 1897); hoary bat, Lasiurus cinereus (Beauvois, 1796); little brown bat, Myotis lucifugus (LeConte, 1831); silver-haired bat, Lasionycteris noctivagans (LeConte, 1831); big brown bat, Eptesicus fuscus (Beauvois, 1796). We predicted DEI would vary with species-specific differences in wing morphology and echolocation call characteristics. From June to August in 2010 and 2011, we passively recorded echolocation calls three to four times per month at eight sites in eastern Ontario, Canada. We found that species’ activity was highest at the edge, regardless of wing morphology and echolocation call characteristics. The DEI for all species was approximately 40 m into both forests and fields. Understanding the effects of DEI on bats will enable more effective acoustic monitoring in future studies and may provide crucial information for management decisions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".