Elk Adopt An Anti-Predatory Strategy, Getting Closer To Hikers In Banff National Park
Bibliographic record
Abstract
Human effects have been described on movements of single species (e.g. bears, wolves, elk), mostly focusing on roads. We tested whether a putatively low-impact activity (hiking) was affecting a predator- prey system involving elk, wolves and bears in Banff National Park (BNP), Canada. We used GPS data for 16 elk, 14 wolves, and 9 bears, in the region where the 3 species were sympatric in May-October, when human use variation was intense. We built a human use model that relied on trail counter data acquired every hour. Wildlife distances to trails were shown to vary across trails of orders-of-magnitude different use, across months, and land cover habitats. In high-use trails, in high-use moths (June, July, August), during daily peaks in activity, elk were closer to trails than wolves. These relationships were stronger in open habitat, where mutual detection was possible. In periods of decreased use, wolves approached trails, while elk moved away. Thus, elk likely adopted an anti-predatory strategy, getting closer to human activity, while bears movements varied individually. Our findings indicate that high numbers of hikers may play a role in shaping prey-predator spatial relations; such effects on the ecosystem are of conservation concern and could be managed.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".