The influence of weather conditions during gestation on life histories in a wild Arctic ungulate
Bibliographic record
Abstract
The internal predictive adaptive response (internal PAR) hypothesis predicts that individuals born in poor conditions should start to reproduce earlier if they are likely to have reduced performance in later life. However, whether this is the case remains unexplored in wild populations. Here, we use longitudinal data from a long-term study of Svalbard reindeer to examine age-related changes in adult female life-history responses to environmental conditions experienced in utero as indexed by rain-on-snow (ROS utero ). We show that females experiencing high ROS utero had reduced reproductive success only from 7 years of age, independent of early reproduction. These individuals were able to maintain the same annual reproductive success between 2 and 6 years as phenotypically superior conspecifics that experienced low ROS utero . Young females born after high ROS utero engage in reproductive events at lower body mass (about 2.5 kg less) than those born after low ROS utero . The mean fitness of females that experienced poor environmental conditions in early life was comparable with that of females exposed to good environmental conditions in early life. These results are consistent with the idea of internal PAR and suggest that the life-history responses to early-life conditions can buffer the delayed effects of weather on population dynamics.
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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) | 0.000 | 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".