A long post-reproductive lifespan is a shared trait among genetically distinct killer whale populations
Bibliographic record
Abstract
The extended female post-reproductive lifespan found in humans and some toothed whales remains an evolutionary puzzle. Theory predicts demographic patterns resulting in increased female relatedness with age (kinship dynamics) can select for a prolonged post-reproductive lifespan due to the combined costs of inter-generational reproductive conflict and benefits of late-life helping. Here we test this prediction using >40 years of longitudinal demographic data from the sympatric yet genetically distinct killer whale ecotypes: resident and Bigg’s killer whales. The female relatedness with age is predicted to increase in both ecotypes, but with a less steep increase in Bigg’s due to their different social structure. Here, we show that there is a significant post-reproductive lifespan in both ecotypes with >30% of adult female years being lived as post-reproductive, supporting the general prediction that an increase in local relatedness with age predisposes the evolution of a post-reproductive lifespan. Differences in the magnitude of kinship dynamics however, did not influence the timing or duration of the post-reproductive lifespan with females in both ecotypes terminating reproduction before their mid-40s followed by an expected post-reproductive period of ~20 years. Our results highlight the important role of kinship dynamics in the evolution of a long post-reproductive lifespan in long-lived mammals, while further implying that the timing of menopause may be a robust trait that is persistent despite substantial variation in demographic patterns among 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 | 0.001 |
| 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 | 0.001 |
| 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".