Lincoln's sparrow (<i>Melospiza lincolnii</i>) increases singing rate in areas with chronic industrial noise
Bibliographic record
Abstract
Animal communication is effective when the transmitted signal reaches the receiver and induces a behavioural response. Increases in anthropogenic noise are altering the ways animals communicate. A common adaptation shown by bird species to anthropogenic noise is song frequency shifts, but birds may also adapt their ability to communicate in noisy environments in other ways. We compared the vocal features of Lincoln's Sparrow Melospiza lincolnii breeding in noisy areas (compressor stations) and in quiet areas in Northern Alberta, Canada. We hypothesized that Lincoln's Sparrow could (1) shift its songs to avoid masking, resulting in higher‐frequency songs, and/or (2) change song allocation (singing rate, number of songs), resulting in improved song transmission. Additionally, we tested the effect of distance and height on song transmission close to a noisy compressor station and in a quiet area to determine if behavioural adaptations might allow more effective song transmission. We did not find frequency shifts between quiet and noisy areas. However, we found birds singing at higher song rates in noisy areas, supporting the song allocation hypothesis. Interestingly, the number of song types sung in noisy sites was fewer than in quiet areas. Distance influenced song transmission differently in noisy and quiet areas, as the signal to noise ratio was substantially less at 20 m in noisy areas than in the control. A higher singing rate may therefore compensate for reduced song transmission by increasing the likelihood of being heard by conspecifics in noisy areas. We found evidence of song adaptation by Lincoln's Sparrow to deal with anthropogenic noise, but whether it is sufficient to facilitate long‐distance communication used to attract females and in male–male interactions remains unknown.
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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 | 0.000 |
| 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".