Longevity is heritable and negatively genetically correlated between the sexes in yellow-bellied marmots
Bibliographic record
Abstract
Longevity, a major fitness component, is heritable in multiple species including both captive and wild populations, and often varies widely between the sexes. The sex-specific genetic architecture of longevity however has rarely been estimated in wild populations, despite its potentially large implication for the evolutionary dynamic of a species. Using a long-term study of wild yellow-bellied marmots, a hibernating rodent, we estimated sex-specific additive genetic variance VA and the cross-sex genetic correlation rfm of longevity. Given the challenges associated with accurately measuring longevity in the wild, we used a new analytical approach based on a Censored Poisson distribution allowing us to integrate measurement errors on longevity in the model. Our approach revealed moderate and comparable VA in both sexes and a strongly negative rfm, albeit with large credible intervals. This contrasts with the results from a classic model with a restricted dataset for which VA in males was estimated as zero, rendering the rfm inestimable and uninterpretable. Our results suggest that studying selection and evolution while focusing on only one sex can lead to erroneous predictions given that, in marmots, selection pressures increasing longevity in one sex would inherently select for the reverse effect in the other sex. Taken together, this suggests the possible presence of a self-reinforcing feedback loop for the development of different life-history strategies among sexes in marmots, with long-lived females producing short-lived males who must maximize early life reproductive success (“live-fast die-young” strategy) and vice-versa. Our study provides rare evidence of heritable longevity in a wild population and highlights how genetic conflicts between the sexes may constrain evolution and help maintain sex-specific genetic variance in fitness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".