A future food boom rescues the negative effects of early-life adversity on adult lifespan in a small mammal
Bibliographic record
Abstract
Early-life adversity, even when transient, can have lasting effects on individual phenotypes and reduce lifespan across species. If these effects can be mitigated by a high-quality later-life environment, then differences in future resources may explain variable resilience to early-life adversity. Using data from over 1000 wild North American red squirrels, we tested the hypothesis that the costs of early-life adversity for adult lifespan could be offset by later-life food abundance. We identified six adversities that reduced juvenile survival in the first year of life, though only one-birth date-had continued independent effects on adult lifespan. We then built a weighted early-life adversity (wELA) index integrating the sum of adversities and their effect sizes. Greater weighted early-life adversity predicted shorter adult lifespans in males and females, but a naturally occurring food boom in the second year of life ameliorated this effect. Experimental food supplementation did not replicate this pattern, despite increasing lifespan, indicating that the buffering effect of a future food boom may hinge on more than an increase in available calories. Our results suggest a non-deterministic role of early-life conditions for later-life phenotype, highlighting the importance of evaluating the consequences of early-life adversity in the context of an animal's entire life course.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".