Acoustic monitoring with miniature drones shows reduced Myotis bat occurrence with altitude and drone movement
Bibliographic record
Abstract
Our understanding of aeroecology is hampered by the challenge of sampling the air column, especially for nocturnal species like bats that forage high in the airspace. Nonetheless, monitoring endangered bat populations is vital for conservation efforts. Drones (unoccupied aerial vehicles) offer a relatively safe, cost-effective, and non-intrusive option for studying aerial wildlife. Here, we present a method for measuring bat distribution in the airspace using miniature drones (Mavic Mini 3 Pro) that are small enough to have negligible impacts on the bats themselves. We investigate how habitat, drone altitude, and drone movement influence bat sampling efficiency. A hovering drone detected more bats per minute than a moving drone for the EPNO complex (Eptesicus fuscus and Lasionycteris noctivagans) and all bats. Thus, we recommend the use of hovering point counts for surveying bats via drone. The Myotis complex (M. septentrionalis, M. lucifugus, and M. leibii) was more frequently present at lower altitudes over the sampled range (0-60 m). We conclude that bat taxa differentially occupy sectors of the air column, and that bat density in the airspace can be efficiently monitored by miniature drones.
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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 | 0.000 | 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".