Allonursing in reindeer,<i>Rangifer tarandus</i>: a test of the kin-selection hypothesis
Bibliographic record
Abstract
Allonursing, the nursing of nonoffspring, is a form of cooperative breeding. To test the kin-selection allonursing hypothesis, we selected 2 experimental groups, based on genetic relatedness, to assess whether evidence of individual and daily patterns of kin-related allonursing would emerge. Each group consisted of 8 mother–offspring pairs, and observers monitored each group over 5 weeks, starting when the offspring were between 8 and 23 days old. One group of 8 mothers was selected to be closely related, and the other group of 8 mothers was selected to be distantly related, based on genetic relatedness. We recorded 1,652 solicitations, of which 869 were nursing bouts and 161 were allonursing bouts. All mothers nursed their own offspring, and 15 of the 16 mothers allonursed. In both groups, 7 of the 8 offspring were allonursed. The offspring of closely related mothers were allonursed more often than the offspring of distantly related mothers, and we found evidence for this pattern at the individual level and daily over 5 weeks. Our results supported the kin-selection hypothesis but not the compensation hypothesis. We suggest that allonursing may have provided adaptive and nutritional benefits related to kin selection, and offspring from the closely related group may have gained more nutritional benefits and more mass than offspring from the distantly related group. We presented evidence that allonursing contributions detected can depend upon the research design. Furthermore, we suggest that the indirect fitness benefits of alloparental care may have been overestimated, and that kin selection alone is not sufficient to explain alloparental care in cooperative breeding social systems.
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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 | 0.000 |
| 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".