Early-life seasonal, weather and social effects on telomere length in a wild mammal
Bibliographic record
Abstract
Early-life environmental conditions can provide a source of individual variation in life-history strategies and senescence patterns. Conditions experienced in early life can be quantified by measuring telomere length, which can act as a biomarker of survival probability. Here, we investigate whether seasonal changes, weather conditions, and group size are associated with early-life and/or early-adulthood telomere length in a wild population of European badgers (Meles meles). We found substantial intra-annual changes in telomere length during the first three years of life (both between and within individuals), with shorter telomere lengths from spring to winter and longer telomere lengths over the winter torpor period. In terms of weather conditions, linked to food availability and foraging success, cubs born in warmer, wetter springs with low rainfall variability had longer early-life (<1 year old) telomere lengths. Additionally, cubs born in groups with more cubs did not have significantly shorter early-life telomeres, providing no evidence of resource constraint from cub competition. We also found that our previously documented positive association between early-life telomere length and cub survival probability remained when social and weather variables were included. Finally, after sexual maturity, in early adulthood (i.e. 12–36 months) we found no significant association between same-sex adult group size and telomere length (i.e. no effect of intra-sexual competition). Overall we show that controlling for seasonal effects is important in telomere length analyses, and that badger telomere length functions as a biomarker that reflects the physiological consequences of early-life adversity and subsequent effects on cub survival probability.
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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".