HABITAT SELECTION BY ELK BEFORE AND AFTER WOLF REINTRODUCTION IN YELLOWSTONE NATIONAL PARK
Bibliographic record
Abstract
Prey species are thought to select habitats to obtain necessary resources while also avoiding predation. We examined whether habitat selection by elk (Cervus elaphus) changed following the reintroduction of wolves (Canis lupus) into Yellowstone National Park in 1995. Using conditional fixed-effects logistic regression to build habitat-selection models, we compared seasonal habitat selection by elk based on weekly elk radiolocations taken in 1985–1990 (without wolves) and 2000–2002 (with wolves). Fire-related habitat changes and climate likely interacted with wolf avoidance in shaping habitat selection by elk. In summer, when wolf activity was centered around dens and rendezvous sites, elk apparently avoided wolves by selecting higher elevations, less open habitat, more burned forest, and, in areas of high wolf density, steeper slopes than they had before wolf reintroduction. In winter, elk did not spatially separate themselves from wolves. Compared to the pre-wolf period, elk selected more open habitats in winter after wolf reintroduction, but did not change their selection of snow water equivalents (SWE) or slope. Elk appear to select habitats that allow them to avoid wolves during summer, but they may rely on other behavioral antipredator strategies, such as grouping, in winter. This study provides evidence that wolves can alter seasonal elk distribution and habitat selection, and demonstrates how the return of wolves to Yellowstone restores important ecosystem processes.
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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 | 0.000 | 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) | 0.001 | 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".