A method for estimating songbird abundance with drones
Bibliographic record
Abstract
Using drones to conduct airborne bioacoustics surveys is a potentially useful new way to estimate the abundance of vocal bird species. Here we show that by using two audio recorders suspended from a quadcopter drone it is possible to estimate distances to birds with precision. In an experimental test, the mean error of our estimated distances to a broadcast song across 11 points between 0 and 100 m away was just 3.47 m. In field tests, we compared 1 min airborne counts with 5 min terrestrial counts at 34 count locations. We found that the airborne counts yielded similar data to the terrestrial point counts for most of the 10 songbird species included in our analysis, and that the effective detection radii were also similar. However, airborne counts significantly under-detected the Northern Cardinal (χ29 = 22.8, post-hoc test P = 0.007), which we attribute to a behavioral response to the drone. Airborne counts work best for species that vocalize close to the ground and have high-frequency-range songs. Under those circumstances, airborne bioacoustics could have several advantages over ground-based surveys, including increased precision, increased repeatability, and easier access to difficult terrain. Further, we show that it is possible to do rapid surveys using airborne techniques, which could lead to the development of much more efficient survey protocols than are possible using traditional survey techniques.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".