Effects of elk group size on predation by wolves
Bibliographic record
Abstract
Wolf-prey research has focused on single-prey systems in North America dominated by moose (Alces alces) or white-tailed deer (Odocoileus virginianus). Elk (Cervus elaphus) are social ungulates and the main prey item of wolves (Canis lupus) in Banff National Park (BNP), Alberta. Grouping behaviour may affect the functional response of predators by changing how predators encounter and kill prey. We studied wolf predation on elk in BNP during the winters of 19971998 and 19981999 and tested how elk group size affected the availability of and encounter rates with elk groups and attack success of wolves. Wolves encountered larger elk groups than expected based on availability, and killed more elk from large groups than expected based on numbers of encounters. Elk group size increased with elk density in BNP. Increased rates of encounter with and success of attacking large elk groups, and the positive group size density relationship may be a mechanism for density-dependent predation. We developed a predation-risk model to test the prediction that grouping will benefit individual elk, given this predation regime. Elk appeared to adopt two different strategies to minimize predation risk: living in small herds that were rarely encountered by wolves or living in large herds that reduced their predation risk through dilution.
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 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) | 0.003 | 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".