Elk population dynamics in areas with and without predation by recolonizing wolves in Banff National Park, Alberta
Bibliographic record
Abstract
Gray wolves (Canis lupus) recolonized the Bow Valley of Banff National Park in the mid-1980s after a 30-year absence. Wolves recolonized one zone of the Bow Valley in 1985 and another in 1991, but human activity excluded wolves from a third zone throughout the study. Elk (Cervus elaphus) are the primary prey of wolves in Banff National Park. We studied the effects of wolf predation, snow depth, elk density, and human-caused mortality on the elk population growth rate in the three different wolf recolonization treatments from 1985 to 2000. We constructed a set of generalized linear models of factors affecting population growth, and used Akaike Information Criteria to guide model selection and inference. In the low wolf predation zone, elk population growth was density-dependent and limited by human-caused mortality. In the zone that wolves recolonized in 1991, elk population growth was limited by the combined effects of snow depth and wolf predation after wolf recolonization, in addition to preexisting mortality caused by humans and other predators. Our correlative approach failed to yield insights into population dynamics in the zone where wolves were present throughout the study. However, by comparing zones we demonstrate important differences in ungulate population dynamics in the presence and absence of wolf predation.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".