Who’s your neighbor? Acoustic cues to individual identity in red squirrel Tamiasciurus hudsonicus rattle calls
Bibliographic record
Abstract
Abstract North American red squirrels Tamiasciurus hudsonicus often produce a loud territorial rattle call when conspecifics enter or invade a territory. Previous playback experiments suggest that the territorial rattle call may indicate an invader’s identity as squirrels responded more intensely to calls played from strangers than to calls played from neighbors. This dear-enemy effect is well known in a variety of bird and mammal species and functions to reduce aggressive interactions between known neighbors. However, although previous experiments on red squirrels suggest some form of individual differentiation and thus recognition, detailed acoustic analysis of potential acoustic cues in rattle calls have not been conducted. If calls function to aid in conspecific identification in order to mitigate aggressive territorial interactions, we would expect that individual recognition cues would be acoustically represented. Our work provides a detailed analysis of acoustic cues to identity within rattle calls. A total of 225 calls across 32 individual squirrels from Sheep River Provincial Park, Kananaskis, AB, Canada, were analyzed with discriminant function analysis for potential acoustic cues to individual identity. Initial analysis of all individuals revealed a reliable acoustic differentiation across individuals. A more detailed analysis of clusters of neighboring squirrels was performed and results again indicated a statistically significant likelihood that calls were assigned correctly to specific squirrels (55%-75% correctly assigned); in other words squirrels have distinct voices that should allow for individual identification and discrimination by conspecifics.
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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 | 0.001 |
| 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".