Testing the predictions of energy allocation decisions in the evolution of life‐history trade‐offs
Bibliographic record
Abstract
Summary Allocating a greater amount of limited resources, such as energy, to current reproduction can reduce the amount of energy available for somatic maintenance and can ultimately impair future breeding success or maternal survival (i.e. cost of reproduction hypothesis). Although there is some support for the cost of reproduction hypothesis in birds, few empirical studies of mammals have demonstrated a trade‐off between current and future reproduction. More importantly, most studies testing ultimate costs have neglected to confirm that the proximate costs of reproduction are high. We experimentally manipulated litter size in a wild population of Columbian ground squirrels for 2 years to examine both the proximate energetic and ultimate fitness (i.e. survival and breeding) costs of reproduction. We predicted that females raising augmented litters would have the highest rates of daily energy expenditure and as a result would experience lower survival rates or future fecundity. Females raising augmented litters weaned more pups, had the highest litter masses at weaning, and had field metabolic rates that were almost 1·5 times greater than females raising control or reduced litters. Contrary to our prediction, there were no negative impacts of greater maternal investment and higher energy expenditure on the probability of maternal survival or future reproduction. Pups from augmented litters grew more slowly during the lactation period were smaller at weaning and had a lower probability of survival over‐winter. Thus, although females were capable of raising more young than they gave birth to without short‐term costs of reduced survival or fecundity, our observations suggest that limitations to litter size are not due to a trade‐off in the allocation of energy, but rather due to the reduced survival of offspring from larger litters. Examining the proximate mechanisms hypothesized to underlie life‐history trade‐offs can be challenging but is critical for a comprehensive understanding of the evolution of life histories.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".