Mass-dependent reproductive strategies in wild bighorn ewes: a quantitative genetic approach
Bibliographic record
Abstract
Abstract In the Ram Mountain bighorn sheep (Ovis canadensis) population, ewes differing by more than 30% in body mass weaned lambs with an average mass difference of only 3%. Variability in adult body mass was partly due to additive genetic effects, but inheritance of weaning mass was weak. Maternal effects could obscure genetic effects in the phenotypic expression of weaning mass, particularly if they reflected strategies of maternal expenditure that varied according to ewe mass. We performed a quantitative genetic analysis to assess genetic and environmental influences on ewe mass and on maternal expenditure. We used the mean daughters/mother regression method and Derivative Free Restricted Maximum Likelihood models to estimate heritability (h2) of ewe mass and indices of maternal expenditure. We found additive genetic effects on phenotypic variation in maternal mass, in lamb mass at weaning (absolute maternal expenditure) and in weaning mass relative to maternal mass at weaning (relative maternal expenditure). Heritability suggests that maternal expenditure has the potential to evolve. The genetic correlation of ewe mass and absolute maternal expenditure was weak, while ewe mass and relative maternal expenditure were strongly negatively correlated. These results suggest additive genetic effects on mass-dependent reproductive strategies in bighorn ewes. Mass-dependent reproductive strategies could affect lamb survival and phenotypic variation in adult mass. As population density increased and reproduction became costlier, small females reduced maternal expenditure more than large females. Constraints on reproductive strategy imposed by variations in resource availability are therefore likely to differ according to ewe mass. A general trend for a decrease in maternal expenditure relative to maternal size in mammals suggests that size-dependent negative maternal effects may be common.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".