The effects of maternal social connectivity and integration on offspring survival in a marmot
Bibliographic record
Abstract
Abstract In social species, maternal social relationships, in addition to direct care, impact offspring survival but much of what we know about these effects comes from studies of obligately social and cooperatively breeding species. Yellow-bellied marmots ( Marmota flaviventer ) are a facultatively social species whose social groups vary in composition, size, and cohesiveness. This natural variation in sociality and cooperative breeding behavior makes yellow-bellied marmots an ideal species within which to study the effects of maternal affiliative and agonistic social behavior on offspring. We used social network analysis to investigate the relationship between maternal social connectivity and integration on offspring summer and yearly survival, with the hypothesis that offspring with more affiliative mothers are more likely to survive than the offspring of more agonistic mothers. However, we found the inverse to be true: pups born to mothers who received more affiliative interactions were less likely to survive while the offspring of mothers who were more highly integrated into agonistic networks had enhanced survival. Overall, maternal social network measures were positively and negatively correlated with offspring survival, indicating that pups are influenced by their mother’s social world, often in contradictory ways. Relative predation risk and colony location also mediated the effects of social relationships on pup survival. This study contributes to a small but growing body of work that demonstrates that specific attributes of sociality have specific consequences and that by adopting an attribute-focused view of sociality we are better able to understand how environmental conditions mediate the costs and benefits of sociality. Lay Summary Maternal social relationships can impact offspring survival but much of what we know about these effects comes from studies of obligately social species. In faculatively social yellow-bellied marmots we found that pups born to mothers who received more affiliative interactions were less likely to survive while the offspring of mothers who were more highly integrated into agonistic networks had enhanced survival. Overall, pups are influenced by their mother’s social world, often in contradictory ways.
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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".