Anthropogenic noise and noise-adjusted signals influence territorial-defense behaviors of male Baird’s sparrows (Centronyx bairdii)
Bibliographic record
Abstract
Anthropogenic noise can constrain the acoustic communication of wildlife species through acoustic masking. However, many species display noise-adjusted signals which are theorized to provide release from acoustic masking. Yet, noise-adjusted signals may alter how receivers perceive and respond to signalers or fail to improve acoustic detection under noisy conditions. Thus, noise and noise-adjusted signals could have consequences for species that rely on acoustic communication for breeding. Baird’s sparrow (Centronyx bairdii) is a grassland bird species that displays noise-adjusted songs. To investigate the potential impacts of noise and noise-adjusted songs on the intrasexual behavior of this species, I conducted repeated measures playback studies (n = 69 dyads) on free-living male Baird’s sparrows. My research took place in the mixed-grass prairies of Southern Alberta during the species breeding season (May to July 2018 and 2019). To simulate territory intrusions in ‘noisy’ and ‘quiet’ conditions, I used a playback design to broadcast unadjusted and noise-adjusted Baird’s sparrow song and oil-well drilling noise to individual male birds. To determine if song or noise treatment influenced male behavior, I compared the number of songs, calls, flybys over the experimental speaker, and song latency for each trial type. Focal male song latency was longer for unadjusted songs broadcast with noise versus without noise, suggesting that noise constrained acoustic detection. However, song latency for noise-adjusted songs broadcast with noise was similar to unadjusted songs broadcast in quiet conditions, suggesting that noise-adjusted songs are easier to detect acoustically in noise. I concluded that noise-adjusted songs partially restore acoustic communication in noisy conditions. The remaining focal male responses did not differ significantly by song treatment, suggesting that the song types are functionally equivalent. However, responses differed significantly between noise treatments. Focal males sang fewer songs whilst increasing alarm call vocalizations and engaged in more flybys under noisy conditions. These results suggest that noise heightens aggression in individuals or that males use different strategies to determine the location and fighting ability of rivals in the presence of noise. While it is uncertain what mechanism(s) underly these behavioral changes, I concluded that anthropogenic noise acts as a disturbance to this species.
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 | 0.000 | 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".