Puppy whines mediate maternal behavior in domestic dogs
Bibliographic record
Abstract
In mammals, offspring vocalizations typically encode information about identity and body condition, allowing parents to limit alloparenting and adjust care. But how do these vocalizations mediate parental behavior in species faced with the problem of rearing not one, but multiple offspring, such as domestic dogs? Comprehensive acoustic analyses of 4,400 whines recorded from 220 Beagle puppies in 40 litters revealed litter and individual (within litter) differences in call acoustic structure. By then playing resynthesized whines to mothers, we showed that they provided more care to their litters, and were more likely to carry the emitting loudspeaker to the nest, in response to whine variants derived from their own puppies than from strangers. Importantly, care provisioning was attenuated by experimentally moving the fundamental frequency ( f o , perceived as pitch) of their own puppies’ whines outside their litter-specific range. Within most litters, we found a negative relationship between puppies’ whine f o and body weight. Consistent with this, playbacks showed that maternal care was stronger in response to high-pitched whine variants simulating relatively small offspring within their own litter’s range compared to lower-pitched variants simulating larger offspring. We thus show that maternal care in a litter-rearing species relies on a dual assessment of offspring identity and condition, largely based on level-specific inter- and intra-litter variation in offspring call f o . This dual encoding system highlights how, even in a long-domesticated species, vocalizations reflect selective pressures to meet species-specific needs. Comparative work should now investigate whether similar communication systems have convergently evolved in other litter-rearing species.
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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 | 0.001 |
| 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".