Noise Source and Individual Physiology Mediate Effectiveness of Bird Songs Adjusted to Anthropogenic Noise
Bibliographic record
Abstract
Anthropogenic noise is a pervasive pollutant altering behaviour of wildlife that communicates acoustically. Some species adjust vocalisations to compensate for noise. However, we know little about whether signal adjustments improve communication in noise, the extent to which effectiveness of adjustments varies with noise source, or how individual variation in physiology varies with response capacity. We played noise-adjusted and unadjusted songs to wild Passerculus sandwichensis (Savannah Sparrows) after measurements of adrenocortical responsiveness of individuals. Playbacks using songs adjusted to noisy environments were effective in restoring appropriate conspecific territorial aggression behaviours in some altered acoustic environments. Surprisingly, however, levels of adrenocortical responsiveness that reduced communication errors at some types of infrastructure were correlated with increased errors at others. Song adjustments that were effective in communicating for individuals with lower adrenocortical responsiveness at pumpjacks were not effective at screwpumps and vice versa. Our results demonstrate that vocal adjustments can sometimes allow birds to compensate for disruptions in communication caused by anthropogenic noise, but that physiological variation among receivers may alter effectiveness of these adjustments. Thus mitigation strategies to minimize anthropogenic noise must account for both acoustic and physiological impacts of infrastructure.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".