Longevity hinders evolutionary rescue through slower growth but not necessarily slower adaptation
Bibliographic record
Abstract
Abstract “Evolutionary rescue” is the process by which a population experiencing severe environmental change avoids extinction through adaptation. Theory and empirical work typically focus on short life histories with non-overlapping generations, leaving longevity’s effects on rescue relatively understudied. Recent models demonstrate that longevity can inhibit rescue through slower phenotypic evolution but have assumptions that may not generalize across life histories. We built a model integrating evolutionary rescue with concepts from life-history theory, particularly the fast-slow pace-of-life continuum. Longevity is modeled by the balance of survival and reproduction with selection acting on survival, allowing for multiple selection episodes throughout the lifespan. We used this model to simulate three life-history strategies along the fast-slow continuum responding to sudden environmental change. Under nearly all simulated conditions, higher longevities (slower pace of life) resulted in more time at low density and increased extinctions. With perfect trait heritability, rates of adaptation were nearly identical across longevities. But at lower heritabilities, longevity allowed for repeated selection and decoupling of mean genotypes and phenotypes, producing a transient phase of rapid phenotypic change. Our results demonstrate that prior findings that longevity slows adaptation do not hold in all cases and are relevant to long-lived conservation targets.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".