Population position along the fast–slow life‐history continuum predicts intraspecific variation in actuarial senescence
Bibliographic record
Abstract
Patterns of actuarial senescence can be highly variable among species. Previous comparative analyses revealed that both age at the onset of senescence and rates of senescence are linked to position of a species along the fast-slow life-history continuum. As there are few long-term datasets of wild populations with known-age individuals, intraspecific (i.e. between-population) variation in senescence is understudied and limited to comparisons of wild and captive populations of the same species, mostly birds and mammals. In this paper, we examined how population position along the fast-slow life-history continuum affects intraspecific variation in senescence in an amphibian, Bombina variegata. We used capture-recapture data collected in four populations with contrasting life-history strategies. Senescence trajectories were analysed using Bayesian capture-recapture models. We show that in populations with fast life histories the onset of actuarial senescence was earlier and individuals aged at a faster rate than individuals in populations with slow life histories. Our study provides one of the few empirical examples of among-population variation in actuarial senescence patterns in the wild and confirms that the fast-slow life-history gradient is associated with both macroevolutionary and microevolutionary patterns of actuarial senescence.
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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 | 0.001 | 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".