Nutrient Balancing by a Wild Browsing Herbivore: Nutritional Geometry of Snowshoe Hares ( <i>Lepus americanus</i> )
Bibliographic record
Abstract
ABSTRACT Browsing herbivores must consider food digestibility while balancing the intake of multiple nutrients (i.e., protein and energy) simultaneously. Nutritional Geometry (NG) is a framework that is used to assess how nutrients interact to impact animal feeding behavior and body condition. Here, we use NG combined with detailed digestibility trials to evaluate how snowshoe hares ( Lepus americanus ), a common boreal browser that experiences 10‐year population cycles, balance energy and protein. We conducted 65 no‐choice and 15 multi‐choice feeding trials on 17 hares in Kluane, Yukon (Canada) during the winters of 2022 and 2023. We tested four diets ranging from the low protein (5.6%) and high fiber content of hare winter food (twigs) to the high protein (16.7%) and low fiber content of rabbit chow. We measured daily intake per kg 0.75 per day in multi‐choice trials and daily intake, weight change, and digestibility in no‐choice trials. We analyzed the effect of diet treatment on each response and the effect of protein and energy intake, in both crude and digestible terms, on feeding rates and weight change. In multi‐choice trials, hares chose a diet balanced in energy and protein, but with a protein content above that in twigs. On single diets, hares were fed to meet a minimum daily digestible energy intake of approximately 1000 kJ/kg 0.75 /day, regardless of protein content, after which digestible protein influenced weight change ( p = 0.02). We found that hares could maintain their weight after they acquired 6 g of digestible protein per kg 0.75 /day. Our results suggest that snowshoe hares choose to consume food items on the basis of the interaction between energy and protein, and these choices influence weight change. Our work supports previous hypotheses that declines in twig quality at peak hare densities could contribute to the subsequent increase in over‐winter weight loss that occurs during the population crash.
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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 | 0.001 |
| 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".