Habitat selection by a focal predator (<i>Canis lupus</i>) in a multiprey ecosystem of the northern Rockies
Bibliographic record
Abstract
Large predators respond to land cover and physiography that maximize the likelihood of encountering prey. Using locations from global positioning system–collared wolves (Canis lupus), we examined whether land cover, vegetation productivity or change, or habitat-selection value for ungulate prey species themselves most influenced patterns of selection by wolves in a large, intact multiprey system of northern British Columbia. Selection models based on land cover, in combination with topographical features, consistently outperformed models based on indexes of vegetation quantity and quality (using normalized difference vegetation index) or on selection value to prey species (moose [Alces americanus], elk [Cervus elaphus], woodland caribou [Rangifer tarandus], and Stone's sheep [Ovis dalli stonei]). Wolves generally selected for shrub communities and high diversity of cover across seasons and avoided conifer stands and non-vegetated areas and west aspects year-round. Seasonal selection strategies were not always reflected in use patterns, which showed highest frequency of use in riparian, shrub, and conifer classes. Patterns of use and selection for individual wolf packs did not always conform to global models, and appeared related to the distribution of land cover and terrain within respective home ranges. Our findings corroborate the biological linkages between wolves and their habitat related to ease of movement and potential prey associations.
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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".