Are trade‐offs really the key drivers of ageing and life span?
Bibliographic record
Abstract
Abstract Current thinking in life‐history theory and the biology of ageing suggests that ageing rates, and consequently life spans, evolve largely as a function of trade‐offs with reproduction. While various evolutionary constraints are generally acknowledged to exist, their potential role in determining ageing rates is rarely considered. This review integrates three types of information to assess the relative importance of constraints and trade‐offs in shaping ageing rates: (a) empirical work on the presence of intraspecific trade‐offs; (b) theoretical work on factors limiting the force of trade‐offs; and (c) consideration of the biological mechanisms of ageing, as currently understood. At the empirical level, evidence for intraspecific trade‐offs is mixed, including some surprising failures to observe a trade‐off in model organisms. At the theoretical level, the presence of multiple currencies and nonlinearity can weaken the strength and/or generality of trade‐offs. Additionally, trade‐offs among lower‐level functions, such as between sources of mortality, can create constraints at higher organizational levels, for example such that reductions in reproduction are unable to produce decreases in ageing rate. In terms of ageing mechanisms, some mechanisms, such as the regulation of IGF‐1 and related pathways, seem to agree quite well with trade‐offs as a driving force; however, other mechanisms, such as dysregulation of the vertebrate stress response and stem cell exhaustion, seem more likely to impose constraints than to mediate trade‐offs. Taken together, these findings suggest that trade‐offs alone are insufficient to understand how ageing rates evolve; instead, both trade‐offs and constraints likely play important roles in shaping evolutionary patterns, with their relative importance varying across taxa. Accordingly, it is time to revisit the broad assumption that survival–reproduction trade‐offs are the key force structuring much of life‐history variation and the evolution of ageing rates. A free Plain Language Summary can be found within the Supporting Information of this article.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".