Attentive red squirrel mothers have faster-growing pups and higher lifetime reproductive success
Bibliographic record
Abstract
Abstract Parental investment theory predicts that observed levels of parental care afforded to offspring are set by the benefits (to offspring quality and survival) relative to the costs (to parental survival or future reproduction). Although difficult to document in mammals, there is often substantial individual-variation in the amount of parental care within species. We measured the impact of individual variation in maternal care (“attentiveness” towards offspring or maternal motivation) on offspring growth and survival in a wild population of North American red squirrels ( Tamiasciurus hudsonicus ). We used latency to return to pups following a nest intrusion as a measure of maternal attentiveness to pups. We found this behavior to be repeatable within individuals suggesting this behavior is a personality trait or a “maternal style”. In this population, postnatal growth rate is important for pup overwinter survival. Pups from large litters grew faster if they had a highly attentive mother, indicating that maternal care behavior can mitigate the trade-off between litter size and offspring growth and potentially improve survival of pups. Additionally, more attentive mothers had slightly higher lifetime reproductive success than less attentive mothers. These results highlight important fitness effects of having a highly attentive mother and show that maternal care behavior can alter a fundamental life history trade-off between offspring quantity and quality. Lay Summary It pays to be attentive to your pups as a squirrel mom. In a long-term study of a wild population of North American red squirrels, we observed repeatable individual variation in maternal attentiveness towards offspring. Mothers who returned faster to pups following a nest intrusion produced faster growing pups and were able to produce larger fast-growing litters. Over their entire lifetime, attentive mothers also had more offspring recruit into the breeding population.
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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) | 0.004 | 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".