The influence of snow on the functional response of grazing ungulates
Bibliographic record
Abstract
The forage intake rate of grazing ungulates is limited either by the rate at which they encounter food items, or the rate at which food items are handled. Whether an ungulate is encounter‐ or handling‐limited influences spatial and temporal depletion of forage, daily time budgets, and ultimately animal condition. Previously, vegetation abundance has been used as a surrogate for an ungulate's encounter rate with food items and related to observed bite rate to determine whether intake rate is encounter‐ or handling‐limited. In temperate climates snow accumulation during winter limits access to vegetation by forcing animals to wade and paw through snow to consume underlying vegetation, increasing the amount of time required to encounter a food item. As a result, an ungulate may be handling‐limited when foraging in a high biomass system under snow‐free conditions, but becomes encounter‐limited when snow accumulates. We derived a model that provides a frame work for estimating the rate at which a grazing ungulate encounters vegetation by considering foraging velocity, vegetation biomass and the time required to paw away snow when present. We then used data from focal observations of 36 wild elk Cervus canadensis wintering on a montane grassland in the Canadian Rockies of Alberta, Canada, to apply our model and estimate encounter rate over a range of vegetation abundance and snow conditions. Using AIC c in a model selection approach we found that an asymptotic regression model of observed bite rate as a function of estimated encounter rate provided a better fit than similar models using only vegetation abundance as the explanatory variable. An asymptotic model suggests elk were handling‐limited in the absence of snow, but became encounter‐limited when snow accumulated. Our results demonstrate the importance of considering the influence of factors other than vegetation abundance on the intake rate of grazing ungulates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".