Ecology and age, but not genetic ancestry, predict fetal loss in a wild baboon hybrid zone
Bibliographic record
Abstract
Abstract Objectives Pregnancy failure and fetal loss represent a major fitness cost for any mammal, particularly those with slow life histories such as primates. Here, we quantified the risk of fetal loss in wild hybrid baboons, including genetic, ecological, and demographic sources of variance. We were particularly interested in testing the hypothesis that hybridization imposes a cost by increasing fetal loss rates. Such an effect would help explain how baboons maintain taxonomic integrity despite interspecific gene flow. Materials and Methods We analyzed pregnancy outcomes for 1,020 pregnancies observed over 46 years in a natural yellow baboon-anubis baboon hybrid zone. Fetal losses and live births were scored based on near-daily records of female reproductive state and the appearance of live neonates. We modeled the probability of fetal loss as a function of a female’s genetic ancestry (based on whole-genome resequencing data), age, number of previous fetal losses, dominance rank, group size, climate, and habitat quality using binomial mixed effects models. Results Female genetic ancestry did not predict the likelihood of fetal loss. Instead, the risk of fetal loss is elevated for very young and very old females. Fetal loss is most robustly predicted by ecological factors, including poor habitat quality and extreme heat during pregnancy. Discussion Our results suggest that gene flow between yellow baboons and anubis baboons is not impeded by an increased risk of fetal loss for hybrid females. Instead, ecological conditions and female age are key determinants of this component of female reproductive success. Research Highlights Female baboons do not experience fetal loss as a cost of hybridization. Heat stress, poor habitat quality, and young and old age elevate the risk of fetal loss, emphasizing roles for ecology and life history in determining birth outcomes. Graphical Abstract Neonate drawings by Emily Nonnamaker.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".