Data from: Experimental traffic noise attracts birds during the breeding season
Bibliographic record
Abstract
Understanding how anthropogenic disturbance affects animal behavior is challenging because observational studies often involve co-occurring disturbances (e.g., noise, lighting, and roadways), and laboratory experiments often lack ecological validity. During the 2016 and 2017 avian breeding seasons, we investigated the effects of anthropogenic noise and light on the singing and spatial behavior of wild birds by independently manipulating the presence of each type of disturbance at 89 sites in an otherwise undisturbed boreal forest in Labrador, Canada. Each treatment was surrounded by an 8-channel microphone array that recorded and localized avian vocalizations throughout the manipulation. We analyzed the effects of noise and light on the timing of the first vocalizations of each species at each array during the dawn chorus, and on the proximity of the vocalizing birds to the disturbance when those songs were produced. We analyzed all species combined, and then conducted separate analyses for the six most common species: Boreal Chickadee, Dark-eyed Junco, Ruby-crowned Kinglet, Swainson’s Thrush, White-throated Sparrow, and Yellow-rumped Warbler. When all species were analyzed together, we found that traffic noise attracted vocalizing birds. There was some evidence that light repelled birds, but this evidence was inconsistent. In our species-specific analyses, Yellow-rumped Warbler sang earlier in response to noise; Swainson's Thrush was attracted to noise and the combination of noise and light, but repelled by light alone. Our study provides some of the first experimental evidence of the independent and combined effects of traffic noise and light on the vocal and spatial behavior of wild birds, and suggests that breeding birds may be attracted to noisy roads where they could be exposed to additional forms of disturbance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.010 |
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".