Winter Prey Selection of Canada Lynx in Northwestern Montana
Bibliographic record
Abstract
ABSTRACT The roles that diet and prey abundance play in habitat selection of Canada lynx ( Lynx canadensis ) in the contiguous United States is poorly understood. From 1998–2002, we back‐tracked radiocollared lynx (6 F, 9 M) for a distance of 582 km and we located 86 kills in northwestern Montana, USA. Lynx preyed on 7 species that included blue grouse ( Dendragapus obscurus ), spruce grouse ( Canachites canadensis ), northern flying squirrel ( Glaucomys sabrinus ), red squirrel ( Tamiasciurus hudsonicus ), snowshoe hare ( Lepus americanus ), least weasel ( Mustela nivalis ), and white‐tailed deer ( Odocoileus virginianus ). Snowshoe hares (69 kills) accounted for 96% (4‐yr average, range = 94–99%) of prey biomass during the sample period. Red squirrels were the second‐most‐common prey (11 kills), but they only provided 2% biomass of the winter diet. Red squirrels contributed little to the lynx diet despite low hare densities. A logistic regression model of snowshoe hare, red squirrel, and grouse abundance, as indexed by the number of track crossings of use and available lynx back‐tracks, was a significant (Wald statistic = 19.03, df = 3, P < 0.001) predictor of habitat use. As we expected, lynx ( P < 0.001) selected use‐areas with higher snowshoe hare abundance compared to random expectation. However, the red squirrel index had a weak ( P = 0.087) negative relationship to lynx use, and grouse was nonsignificant ( P = 0.432). Our results indicate that lynx in western Montana prey almost exclusively on snowshoe hares during the winter with little use of alternative prey. Thus, reductions in horizontal cover for hares would degrade lynx habitat.
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 | 0.000 |
| 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".