High early lactational synchrony within baboon groups predicts increased female-female competition and infant mortality
Bibliographic record
Abstract
Abstract Female-female competition may be particularly acute when many females in the same social group have dependent young at the same time, with potential negative consequences for offspring survival. Here, we used more than four decades of data on wild baboons ( Papio sp.) in Amboseli, Kenya, to examine the effects of ‘early lactational synchrony’ (the proportion of females in a group with an infant <90 days old) on female-female agonistic interactions and infant survival. Because early lactation is energetically demanding for mothers and high-risk for infants, we expected early lactational synchrony to intensify both female-female aggression and maternal association with males, who can buffer infants from conspecific harassment. In support of these predictions, when early lactational synchrony was high, new mothers initiated more agonistic contests. Further, high-ranking females increased their time associating with adult males, which may limit the ability of low-ranking females to receive male protective services and may have downstream consequences for female foraging efficiency. Finally, high early lactational synchrony strongly predicted infant mortality. This association may result from both aggression among adult females and infanticidal behavior by peripubertal females. Our findings provide evidence that synchronous reproduction alters competitive regimes and compromises reproductive outcomes even in nonseasonal breeders.
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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 | 0.001 | 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) | 0.003 | 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".