The nutritional and foraging ecology of a cyclical herbivore (Lepus americanus) in winter
Bibliographic record
Abstract
Environmental conditions can affect both an animal’s energy and nutrient (e.g., protein and minerals) requirements and its ability to fulfill them via foraging, ultimately affecting fitness. In winter, browsing (i.e., twig-eating) herbivores experience the lowest food qualities (i.e., nutritional compositions) and quantities and cold temperatures. Snowshoe hares (Lepus americanus), which range across the Canadian boreal forest and exhibit 10-year population cycles, lose weight over winter, implying they may be bottom-up (i.e., food-related) limited. However, whether winter conditions and population densities contribute to nutritional strain in snowshoe hares remains lesser understood. In this thesis, I investigate how protein-energy ratios within foods affect feeding rates (g/day) and weight change (%) of temporarily captive hares, how snow depth (cm) affects twig availability (g/m2), and how food supplementation interacts with environmental conditions, including twig availability and population density (hare/ha), to affect the foraging effort (hr/day), protein intake (%), and space use (ha) of free-ranging hares. I found that hares fed to meet a minimum digestible energy intake, after which protein intake also influences weight change. By considering energy and protein intake simultaneously, my finding unifies conflicting results from older studies. Using trail cameras, I found that twig availability in winter increased as snow accumulated up to 30 cm and then declined dramatically. I also found that only 20% of available twigs hare enough protein to maintain hare weight. With data from free ranging food supplemented and control hares over six winters (2015 – 2021), I found that hares foraged more as twig availability and ambient temperatures, whether they were supplemented with food or not. This result implies that hares forage less when foraging yields less return or greater heat loss. This same study also showed that hares consumed more protein when twigs were more available and when hare densities were lower, indicating that hares could be more protein limited at low food density, either by way of area or per capita. Lastly, I found that hare home ranges shrank as hare densities increased, and that food supplemented hares were more conservative with spaces use at high densities than controls. I suggest that reduced space use at high densities is a strategy to minimize competition and maintain resource familiarity. This thesis provides evidence that high population densities, deep snow, and low temperatures in winter can exaggerate bottom-up limitation in snowshoe hares.
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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".