Direct and indirect effects of early-life environment on lifetime fitness of bighorn ewes
Bibliographic record
Abstract
Cohort effects, when a common environment affects long-term performance, can have a major impact on population dynamics. Very few studies of wild animals have obtained the necessary data to study the mechanisms leading to cohort effects. We exploited 42 years of individual-based data on bighorn sheep to test for causal links between birth density, body mass, age at first reproduction (AFR), longevity and lifetime reproductive success (LRS) using path analysis. Specifically, we investigated whether the effect of early-life environment on lifetime fitness was the result of indirect effects through body mass or direct effects of early-life environment on fitness. Additionally, we evaluated whether the effects of early-life environment were dependant on the environment experienced during adulthood. Contrary to expectation, the effect on LRS mediated through body mass was weak compared to the effects found via a delay in AFR, reduced longevity and the direct effect of birth density. Birth density also had an important indirect effect on LRS through reduced longevity, but only when adult density was high. Our results show that the potential long-term consequences of a harsh early-life environment on fitness are likely to be underestimated if investigations are limited to body mass instead of fitness at several life stages, or if the interactions between past and present environment are ignored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".