The impact of anthropogenic noise on individual identification via female song in Black-capped chickadees (Poecile atricapillus)
Bibliographic record
Abstract
When anthropogenic noise occurs simultaneously with an acoustic signal or cue, it can be difficult for an animal to interpret the information encoded within vocalizations. However, limited research has focused on how anthropogenic noise affects the identification of acoustic communication signals. In songbirds, research has also shown that black-capped chickadees (Poecile atricapillus) will shift the pitch and change the frequency at which they sing in the presence of anthropogenic, and experimental noise. Black-capped chickadees produce several vocalizations; their fee-bee song is used for mate attraction and territorial defence, and contains information about dominance hierarchy and native geographic location. Previously, we demonstrated that black-capped chickadees can discriminate between individual female chickadees via their fee-bee songs. Here we used an operant discrimination go/no-go paradigm to discern whether the ability to discriminate between individual female chickadees by their song would be impacted by differing levels of anthropogenic noise. Following discrimination training, two levels of anthropogenic noise (low: 40 dB SPL; high: 75 dB SPL) were played with stimuli to determine how anthropogenic noise would impact discrimination. Results showed that even with low-level noise (40 dB SPL) performance decreased and high-level (75 dB SPL) noise was increasingly detrimental to discrimination. We learned that perception of fee-bee songs does change in the presence of anthropogenic noise such that birds take significantly longer to learn to discriminate between females, but birds were able to generalize responding after learning the discrimination. These results add to the growing literature underscoring the impact of human-made noise on avian wildlife, specifically the impact on perception of auditory signals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".