Response of Wolves to Corridor Restoration and Human Use Management
Bibliographic record
Abstract
Corridor restoration is increasingly being used to connect habitat in mountainous areas where rugged topography and increasing human activity fragment habitat.Wolves (Canis lupus) are a conservation priority because they avoid areas with high levels of human use and are ecologically important predators.We examined how corridor restoration through a golf course changes the distribution of wolves and their prey in Jasper National Park, Alberta, Canada.We followed and recorded wolf paths in the snow both within the corridor and in the surrounding landscape before and after a corridor was re-established.Track transects were used to estimate prey abundance and snow depths, and trail counters measured human activity.We compared resources on wolf paths to available movement routes using conditional logistic regression and also compared resources used by wolves before and after restoration.We addressed potential confounding effects of prey abundance, snow depths, and levels of human use by testing for changes in these variables.Prior to restoration, wolves traveled around the golf course and used the mountainside to connect valley-bottom habitat.Conversely, elk (Cervus elaphus) densities were highest in the golf course.After restoration, wolves shifted most of their movement to the golf course corridor, whereas elk dispersed along the corridor and mountainside.When traveling through the study area, wolves selected for areas with high prey abundance, low elevations, and low levels of human activity.Corridor restoration increased the area of high quality habitat available to wolves and increased their access to elk and deer at low elevations.Our results corroborate other studies suggesting that wolves and elk quickly adapt to landscape changes and that corridor restoration can improve habitat quality and reduce habitat fragmentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".