Influences of Individual and Environmental Factors on Body Condition in Female Elk
Bibliographic record
Abstract
Ungulate body condition is often considered to reflect the nutritional resources on the landscape but is ultimately influenced by more than forage as body condition integrates both energetic costs and benefits. Variation in female body condition is driven by both individual and environmental factors, such as age and reproductive effort or forage resources and predation risk, respectively. Our research evaluates how individual and environmental factors influence ingesta-free body fat (IFBF) in female elk (Cervus canadensis) to improve understanding and interpretation of body condition data. We collected seven years (2015-2021) of IFBF data from monitored and recaptured female elk (n = 139) in the Ya Ha Tinda (YHT) population in Alberta, Canada. We determined the best fitting generalized linear mixed-effects model to explain IFBF as a function of individual and environmental factors. Our top model included only prior summer calf survival as a predictor variable, with the second model (DAICc = 1.42) including both prior summer calf survival and average prior summer forage biomass. Our final top model predicts that a female elk whose calf survives the previous summer will have 3.28 percentage points (95% CI: 2.38, 4.19) lower body fat levels in winter compared to a female elk whose calf did not survive the summer. The importance of prior summer calf survival as an explanatory variable and the large size of its effect indicates that changes in reproductive costs driven by predation influence variation in female body fat more significantly than environmental factors like access to forage in this system. Our research helps scientists and managers interpret variation in ungulate body condition data and understand the important effects of juvenile survival on adult ungulate female body condition in the context of expanding predator communities across North America.
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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 | 0.001 | 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 | 0.001 | 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".