Are the negative effects of roads on breeding birds caused by traffic noise?
Bibliographic record
Abstract
Summary 1. The effects of roads on wildlife populations are widespread and well documented. Many studies have shown that bird abundance, occurrence and species richness are reduced near roads, with the largest reductions where traffic levels are high. Negative correlations have been reported between bird richness/abundance and traffic noise but the possible causes of road effects are inter‐correlated. It is important to disentangle the different effects so that appropriate mitigation measures can be implemented. 2. We tested the hypothesis that traffic noise is a key negative effect by testing three predictions: (i) bird richness/abundance should reach a maximum at the same distance from roads that traffic noise reaches a minimum; (ii) the effect of traffic noise on bird richness/abundance should be stronger than the effect of distance from the road on bird richness/abundance; and (iii) sites with more traffic noise at a given distance from the road should show lower bird richness/abundance than sites with less traffic noise at the same distance. 3. We collected breeding bird occurrence and traffic noise data along twenty 600‐m transects perpendicular to roads at 10 high‐traffic road sites. 4. Traffic noise decreased and bird species richness increased with increasing distance from the roads. However, none of the predictions derived from the traffic noise hypothesis was supported. 5. Synthesis and applications. Our results suggest that traffic noise is not the main cause of the negative relationship between bird species richness/abundance and proximity to roads. Instead, traffic mortality may be the main mechanism causing this relationship. We suggest that mitigation of road impacts on birds should focus mainly on reducing mortality rather than reducing traffic noise. In particular, engineering road surfaces, tyres and vehicle engines to reduce noise would not mitigate road effects; instead, structures to keep birds away from roads or force them to fly above the traffic would be more effective.
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 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".