Contributions of environmental conditions and sound characteristics to differences in perceptibility: Recommendations for passive acoustic monitoring
Bibliographic record
Abstract
Abstract Passive acoustic monitoring is increasingly used in ecological research to study a wide variety of taxa; however, properly accounting for perceptibility, a component of detection probability, has been challenging because sound attenuation is influenced by numerous variables in the surrounding environment and the rate of sound attenuation is not typically quantified. We used sound playback experiments to investigate the effect of several environmental variables including vegetation type and weather on perceptibility of a variety of sounds and species. We also investigated species-specific variables such as sound frequency, syllable rate, and bandwidth. We quantified the amount of bias in perceptibility resulting from species sound characteristics and vegetation type and estimated perceptibility and the area surveyed for different combinations of species and habitat type. We found that distance was the strongest predictor of perceptibility, but that vegetation type significantly influenced perceptibility, particularly when comparing open versus closed vegetation classes. Sound frequency and bandwidth of species were also important predictors of perceptibility. We found that ignoring variation in perceptibility due to excess sound attenuation from vegetation could bias area surveyed during acoustic surveys by up to 400% (mean = 167.4%). We conclude that other than distance, perceptibility is influenced most by species-specific vocalization traits and by the habitat these vocalizations transmit through. We provide recommendations for incorporating these findings into future research to account for varying perceptibility and improve the accuracy of bird monitoring and research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".