Life-history strategies of North American elk: trade-offs associated with reproduction and survival
Bibliographic record
Abstract
The principle of energy allocation states that individuals should attempt to maximize fitness by allocating resources optimally among growth, maintenance, and reproduction. Such allocation may result in trade-offs between survival and reproduction, or between current and future reproduction. We used a marked population of North American elk (Cervus elaphus) to determine how energetic costs of reproduction in the current year affect survival and reproduction in the subsequent year. Using a multistate mark-recapture model we examined the influence of individual and environmental variation on trade-offs between these 2 life-history traits. We observed no difference in survival probabilities between pregnant and nonpregnant individuals or as a function of recruiting an offspring. Nonetheless, there was a negative effect of recruiting an offspring in the current year on becoming pregnant the following year. Increased body condition, and higher precipitation, contributed to greater probabilities of becoming pregnant in a particular year regardless of reproductive state and previous recruitment. Costs associated with reproduction led to a reduced probability of future reproduction rather than a reduction in survival. These findings are consistent with risk-sensitive reproductive allocation, where adult survival is maintained through variation in reproductive effort resulting in high and stable adult survival and more-variable reproduction.
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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".