Shifting song frequencies in response to anthropogenic noise: a meta-analysis on birds and anurans
Bibliographic record
Abstract
Anthropogenic noise has been shown to alter the transmission environment and distort acoustic signals, prompting vocalizing species to use compensatory mechanisms. Through a meta-analysis we investigated the relative importance of biological and contextual factors predisposing species to shift their singing/calling frequencies in response to anthropogenic noise. We gathered data from 36 studies, synthesizing information on more than 160 experiments and 60 bird and anuran species. To estimate the breadth of frequency shift, we calculated a standardized effect size using Hedges’ g. We fitted a multilevel linear mixed-effect model on g as the dependent variable weighted by its inverse variance, with typical frequency, body mass, experimental condition, and noise source type as independent terms. Our results reveal broader shifts in smaller bird species when compared with bigger species, an effect that was emphasized in the low-frequency component of the song spectrum. Birds increased their dominant frequencies when confronted to anthropogenic noise, whereas anurans were less prone to such shifts. Human-altered acoustic environments can be considered a novel selective force impelling change to the communication patterns of many vocalizing species.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".