Soundscapes in our Neighborhood: Using Autonomous Recording Devices to Capture Diversity Patterns along a Land-use Gradient
Bibliographic record
Abstract
Soundscapes are an effective way to monitor what species are present in a certain area. Observing habitat preferences of different species allows for more directed conservation and management strategies. We aimed to observe differences in biophonic and anthrophonic soundscapes along an urbanization gradient in Clinton County, New York. In fall 2025, autonomous recording devices (ARUs) were set to continuously record sound for 3 days at sites concurrently being surveyed for ongoing owl research. Birdnet analyzer AI algorithm was used to identify and distinguish between different calls and anthropogenic sounds. The most similar (71.4%) sites were Wickham Marsh and Port Douglas, while the Barracks golf course and Peru were the least similar (13.8%). The most anthropogenic noise were engines at the Barracks golf course, a highly urban site adjacent to a major airport, while the least was recorded at the rural Point au Roche State Park. The highest species richness was found at Clinton Community College while the lowest was found at Peru. Common species across sites were blue jays (Cyanocitta cristata), Canada geese (Branta canadensis), great horned owls (Bubo virginianus), and American toads (Anaxyrus americanus) for passerines, waterfowl, raptors, and others, respectively. Passerines were the most diverse community and other Orders reflected microhabitat conditions at sites. High human disturbance may reduce local species richness, so minimizing unnecessary disturbances may increase richness. Suitable habitats need to be heterogeneous to support a greater range of species. Soundscape analysis is a useful tool to monitor biodiversity and guide conservation efforts.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".