Nutritional influences on breeding dynamics in elk
Bibliographic record
Abstract
Nutrition in summer and early autumn may influence pregnancy rates and recruitment in ungulate herds in temperate regions, yet this influence on the reproductive endocrinology of wild ungulates is poorly understood. We examined the effects of 3 levels of summerautumn nutrition on timing of breeding, pregnancy rates, and concentration of fecal progestagens (P4) in 30 captive female elk (Cervus elaphus nelsoni). Digestible energy (DE) content for females on the high-nutrition treatment was maintained at 12.35 kJ/g, while DE content for females on the medium- and low-nutrition treatments was gradually reduced to 10.89 and 9.42 kJ/g, respectively, by the end of the breeding season (all feed was offered ad libitum) to mimic the natural decline in forage quality during summer and autumn. In addition, we used three females as a control; they were maintained on a high-nutrition diet and kept separate from a bull. Based on fecal P4 concentrations and visual observations, 90, 90, and 10% of the elk on the high-, medium-, and low-nutrition treatments bred, respectively. Nutritional restriction delayed breeding by an average of 8 and 28 days in the medium- and low-nutrition animals. Females in the low- and medium-nutrition treatments excreted significantly higher P4 concentrations prior to breeding than did animals in the high-nutrition treatment. Diet failed to affect P4 concentration during pregnancy, but we found evidence that P4 concentrations were positively correlated with body condition (ingesta-free body fat content) during early gestation. Females that did not breed failed to exhibit estrous behavior, and hormone-excretion profiles confirmed that these animals failed to ovulate. These results suggest that inadequate nutrition in summer and autumn reduce pregnancy rates by preventing estrus and ovulation rather than by inducing early-embryo mortality.
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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 | 0.000 |
| 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) | 0.001 | 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".