Do you hear what I hear? Implications of detector selection for acoustic monitoring of bats
Bibliographic record
Abstract
Summary The probability of detecting the echolocation calls of bats is affected by the strength of the signal as well as the directionality and frequency response of the acoustic detectors. Regardless of the research question, it is important to quantify variation in recording system performance and its impacts on bat detection results. The purpose of this study was to compare the detection of echolocation calls among five commonly used bat detectors: AnaBat SD2 (Titley Scientific), Avisoft UltraSoundGate 116 CM16/CMPA (Avisoft Bioacoustics), Batcorder 2·0 (ecoObs), Batlogger (Elekon AG) and Song Meter SM2BAT (Wildlife Acoustics). We used playback of synthetic calls to optimize detection settings for each system. We then played synthetic signals at four frequencies (25, 55, 85 and 115 kHz) at 5‐m intervals (5–40 m) and three angles (0°, 45°, 90°) from the detectors. Finally, we recorded free‐flying bats (Lasiurus cinereus), comparing the number of calls detected by each detector. Detection was most affected by the frequency dominating the signal and the distance from the source. The effect of angle was less apparent. In the synthetic signal experiment, Avisoft and Batlogger outperformed other detectors, while Batcorder and Song Meter performed similarly. Batlogger performed better than the other detectors at angles off‐centre (45° and 90°). AnaBat detected the fewest signals and none at 85 kHz or 115 kHz. Avisoft detected the most signals. In the free‐flying bat experiment, Batlogger recorded 93% of calls relative to Avisoft, while AnaBat, Batcorder and Song Meter recorded 40–50% of the calls detected by Avisoft. Numerous factors contribute to variation in data sets from acoustic monitoring; our results demonstrate that choice of detector plays a role in this variation. Differences among detectors make it difficult to compare data sets obtained with different systems. Therefore, the choice of detector should be taken into account in designing studies and considering bat activity levels among studies using different detectors.
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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 | 0.006 | 0.033 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".