DIFFERENTIAL HABITAT SELECTION BY MOOSE AND ELK IN THE BESA-PROPHET AREA OF NORTHERN BRITISH COLUMBIA
Bibliographic record
Abstract
Elk (Cervus elaphus) populations are increasing in the Besa-Prophet area of northern British Columbia, coinciding with the use of prescribed burns to increase quality of habitat for ungu- lates. Moose (Alces alces) and elk are now the 2 large-biomass species in this multi-ungulate, multi- predator system. Using global positioning satellite (GPS) collars on 14 female moose and 13 female elk, remote-sensing imagery of vegetation, and assessments of predation risk for wolves (Canis lupus) and grizzly bears (Ursus arctos), we examined habitat use and selection. Seasonal ranges were typi- cally smallest for moose during calving and for elk during winter and late winter. Both species used largest ranges in summer. Moose and elk moved to lower elevations from winter to late winter, but subsequent calving strategies differed. During calving, moose moved to lowest elevations of the year, whereas elk moved back to higher elevations. Moose generally selected for mid-elevations and against steep slopes; for Stunted spruce habitat in late winter; for Pine-spruce in summer; and for Subalpine during fall and winter. Most recorded moose locations were in Pine-spruce during late winter, calv- ing, and summer, and in Subalpine during fall and winter. Elk selected for mid-elevations except in summer and for steep slopes in late winter. Use and selection of 3 habitat classes were prominent for elk: Deciduous and Elymus burns, and Subalpine. Highest overlap between moose and elk occurred during fall and winter when both species used and strongly selected for Subalpine habitat. Neither elk nor moose selected areas to minimize the risk of wolf predation, but elk selected areas with lower risk of predation by grizzly bears and higher vegetation quality during calving and summer. ALCES VOL. 44: 41-63 (2008)
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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".