FACTORS CORRELATED WITH FORAGING BEHAVIOR OF WOLVES IN AND NEAR GLACIER NATIONAL PARK, MONTANA
Bibliographic record
Abstract
We examined prey selection, search distance (measured as km traveled/kill), and spatial use of recolonizing wolves (Canis lupus) in a multi-prey system in northwestern Montana, USA, and southeastern British Columbia, Canada, from 1986 to 1996. Our objective was to explore factors affecting these parameters to better understand wolf–prey relationships of recolonizing wolves. Within white-tailed deer (Odocoileus virginianus) winter ranges, wolves selectively killed elk (Cervus elaphus) over deer. Number of wolves (r = 0.67, P = 0.03), year (r = 0.68, P = 0.02), and possibly human hunter-days/elk harvested (r = 0.55, P = 0.08) were positively correlated with variation in proportion of deer killed by wolves annually. Outside of severe winters, white-tailed deer, elk, and moose (Alces alces) appeared to be equally vulnerable to wolf predation. Search distance of wolves varied by up to 12 times annually. Snow depth (r = 0.73, P = 0.03) and proportion of total kills by wolves that were deer (r = 0.66, P = 0.06) were negatively correlated with the annual variation in the total search distance of wolves. Search distance per wolf was correlated negatively with year (r = 0.66, P = 0.06) and exponentially with hunter-days/elk harvested (r = 0.70, P = 0.04). Space use by wolves may have been in response to local changes in deer abundance. Wolves appeared to select the most profitable prey species. Severe winters and wolf selection for deer, coinciding with a decrease in elk numbers, increased wolf hunting efficiency by reducing search distance. Further research is needed to determine whether reduced search distance equates to increased kill rates by wolves in this system. Based on the time, expense, and difficulty of gathering data on wolf search distance in this sytem, however, we recommend against assessing impacts of wolves on prey via measuring kill rate. Rather, we suggest monitoring impacts of recolonizing wolves by directly assessing cause-specific mortality and recruitment rates of prey species.
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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) | 0.000 | 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".