The influence of variable snowpacks on habitat use by mountain caribou
Bibliographic record
Abstract
Mountain caribou (Rangifer tarandus caribou) in southeastern British Columbia subsist for most of the winter on arboreal hair lichen, mostly Bryoria spp. Foraging occurs mainly in old subalpine fir (Abies lasiocarpa) forests near treeline. Here, the lower limit of Bryoria in the canopy is dictated by snowpack depth because hair lichens die when buried in snow. Bryoria is often beyond the reach of caribou in early winter, prompting caribou to move downslope to where lichen occurs lower in the canopy and other foraging modes are possible. Snowpacks are normally deep enough by late winter that caribou can reach Bryoria where it is most abundant, at high elevations. Extending this to inter-annual comparisons, Bryoria should be less accessible during late winter of low-snow years following normal winters, or of normal to low-snow years after deep-snow winters. We hypothesized that when maximum snowpack in late winter is low relative to the deepest of the previous 5 years, mountain caribou will use lower elevations to facilitate foraging (“lichen-snow-caribou” or LSC hypothesis). We tested this with late-winter data from 13 subpopulations. In the dry climatic region generally and for minor snowfall differences in wet and very wet regions, caribou did not shift downslope or in fact were at higher elevations during relatively low-snow years, possibly reflecting the ease of locomotion. The LSC hypothesis was supported within wet and very wet regions when snowpacks were about 1 m or more lower than in recent years. Elevation declined by 300 m (median) to 600 m (25th percentile) for snowpack differences of at least 1.5 m. Greater use of lodgepole pine and western hemlock stands sometimes also occurred. Management strategies emphasizing subalpine fir stands near treeline should be re-examined to ensure protection of a broader range of winter habitats used by caribou under variable snowpack conditions.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".