Worldwide Soundscapes: a synthesis of passive acoustic monitoring across realms
Bibliographic record
Abstract
Abstract The urgency for remote, reliable, and scalable biodiversity monitoring amidst mounting human pressures on climate and ecosystems has sparked worldwide interest in Passive Acoustic Monitoring (PAM), but there has been no comprehensive overview of its coverage across realms. We present metadata from 358 datasets recorded since 1991 in and above land and water constituting the first global synthesis of sampling coverage across spatial, temporal, and ecological scales. We compiled summary statistics (sampling locations, deployment schedules, focal taxa, and recording parameters) and used eleven case studies to assess trends in biological, anthropogenic, and geophysical sounds. Terrestrial sampling is spatially denser (42 sites/M·km 2 ) than aquatic sampling (0.2 and 1.3 sites/M·km 2 in oceans and freshwater) with only one subterranean dataset. Although diel and lunar cycles are well-covered in all realms, only marine datasets (65%) comprehensively sample all seasons. Across realms, biological sounds show contrasting diel activity, while declining with distance from the equator and anthropogenic activity. PAM can thus inform phenology, macroecology, and conservation studies, but representation can be improved by widening terrestrial taxonomic breadth, expanding coverage in the high seas, and increasing spatio-temporal replication in freshwater habitats. Overall, PAM shows considerable promise to support global biodiversity monitoring efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".