Lifetime fitness and annual survival are heritable and highly genetically correlated in a wild primate population
Bibliographic record
Abstract
The additive genetic variance (VA) of fitness quantifies the expected response to selection. Lifetime reproductive success (LRS) is an effective metric of lifetime fitness in animal population, while time-limited fitness metrics such as annual survival or fertility can help to identify which fitness component - e.g., annual survival or fertility - harbor the most VA in fitness. Here we estimated the VA and heritability (h2) of LRS and three time-limited fitness metrics in a wild female baboon population in Kenya. The most heritable metrics were LRS (h2=0.25 [0.18, 0.34]) and annual survival (h2=0.23 [0.15, 0.33]). By further partitioning LRS, we were able to show that nearly all the VA for LRS was attributable to survival to first successful reproduction. Furthermore, all fitness metrics examined were highly genetically correlated with each other, supporting the use of time-limited metrics when LRS data are limited. Our analyses predicted faster phenotypic evolution than we have observed, raising the possibility that environmental or social variables masked responses to selection or inflated estimated VA. Overall, our findings reveal a substantial genetic contribution to variation in survival, and in turn, to fitness and contemporary evolution in a long-lived animal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".