Winter habitat relationships of deer and elk in the temperate interior mountains of British Columbia
Bibliographic record
Abstract
Recent work has examined ultimate factors limiting deer (Odocoileus spp.) and elk (Cervus elaphus) populations during winter, such as temperature and forage, but there has been inadequate examination of the influence of snow, especially in concert with foraging decisions. We examined deer and elk habitat selection on winter range in the temperate mountains of southeastern British Columbia. The life histories of radiocollared mule deer (O. hemionus) and elk included seasonal elevational migrations (1,000–1,400 m) and long-distance movements (up to 50–63 km, respectively). Late-winter, deep-snow habitat is limited in wet, mountainous environs and may explain the relatively low densities of ungulates present. Snow-track transects conducted during late winter suggested that deer (mule deer and white-tailed deer [O. virginianus] combined) avoided areas with >40 cm of snow and elk areas with >50 cm. Late-winter snow depth was positively related to elevation and negatively related to slope and solar radiation (hours/day), all of which can be obtained from existing databases and used to map relative late-winter snow depth. The snow-depth model can be used to map potential winter range regardless of current vegetation cover. During late winter deer selected older forests and stands with greater amounts of Douglas-fir (Pseudotsuga menziesii) in the overstory in forested sites. Older stands probably were selected because they had lower snow depths, while mature Douglas-fir trees offered more litterfall forage than other tree species. In contrast, elk selection among forested stands was weak, which suggested that forested stands were not preferred portions of their late-winter habitat. Track data during late winter confirmed that both deer and elk also used areas with low canopy cover, likely to acquire browse. We demonstrated that it is possible to map potential winter range using topographic variables as surrogates for relative snow depth, and we present a model applicable to portions of the temperate interior mountains. We suggest it is important to consider both forage production and snow interception in habitat management because winter energy budgets are a balance between nutrient intake and cost of locomotion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".