Field test of an affordable, portable, wireless microphone array for spatial monitoring of animal ecology and behaviour
Bibliographic record
Abstract
Summary 1. Using arrays of microphones, biologists can monitor the position of free‐living animals based on the sounds they produce. Microphone array technology exploits differences in sound arrival times at each microphone to calculate an animal’s position. This technology provides new opportunities for studying animal ecology and behaviour and has many advantages over tracking technologies that require capturing animals and fitting them with external devices, or technologies that focus on one individual in isolation of the activities of nearby animals. 2. The efficacy of microphone arrays for triangulating the position of wild animals has been established through previous studies. Yet widespread use of microphone array technology has been limited by many factors: arrays are expensive, custom manufactured, and cumbersome. Consequently, microphone arrays are used infrequently, in spite of their transformative potential for studying animal ecology and behaviour. 3. We conducted a field test of a new wireless microphone array system that has multiple advantages over previous systems: it is relatively inexpensive, commercially available, includes an integrated global positioning system (GPS) for time‐synchronizing microphones, and it is small enough to fit in a backpack. We set up an array of four stereo recorders (each with a pair of stereo microphones) at 12 sites and tested the system’s accuracy for estimating the location of loudspeakers broadcasting 25 types of bird, mammal and frog sounds. 4. We found that this system produced accurate location estimates based on multi‐channel recordings of many types of acoustic signals. The average location accuracy was 1·87 ± 0·13 m, on par with cable‐based microphone array systems. Location accuracy was significantly higher when the recorders were closer together and when sounds were broadcast inside the area bounded by the microphones. Accuracy tended to be higher in field vs. forest habitats. 5. We discuss how this system may be used to enhance studies of animal ecology and behaviour across a wide range of contexts. As with previous arrays, this system will allow researchers to monitor animals that produce distinctive acoustic signals. In contrast to previous microphone arrays, this system is affordable, portable and commercially available. Consequently, this system stands to dramatically enhance research on wild, free‐living animals.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".