Using playback of territorial calls to investigate mechanisms of kin discrimination in red squirrels
Bibliographic record
Abstract
Kin recognition can facilitate kin selection and may have played a role in the evolution of sociality. Red squirrels (Tamiasciurus hudsonicus) defend territories using vocalizations known as rattles. They use rattles to discriminate kin, though the mechanism underlying this ability is unknown. Our objective was to distinguish between the mechanisms of prior association, where animals learn the phenotypes of kin they associate with early in life, and phenotype matching/recognition alleles, where animals use a template to match phenotypes, thereby allowing them to recognize kin without an association early in life. We used audio playbacks to measure the responses of squirrels to rattles from familiar kin, unfamiliar kin, and non-kin. Initial analyses revealed that red squirrels did not discriminate between familiar and unfamiliar kin, but also did not discriminate between kin and non-kin, despite previous evidence indicating this capability. Post hoc analyses showed that a squirrel’s propensity to rattle in response to playback depended on an interaction between relatedness and how the playback stimuli had been recorded. Red squirrels discriminated between rattles from close kin (r = 0.5) and rattles from non-kin (r < 0.125) when the rattles were recorded from provoked squirrels. Squirrels did not exhibit kin discrimination in response to unsolicited rattles. Once we accounted for how the stimuli had been recorded, we found no difference in the responses to familiar and unfamiliar kin. Our study suggests that kin discrimination by red squirrels may be context dependent.
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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 | 0.001 | 0.001 |
| 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".