Effects of vegetation and topography on snowshoe hare relative abundance at the southern range periphery
Bibliographic record
Abstract
Abstract The conservation of snowshoe hares (Lepus americanus) and their habitat is a major focus in the American West, largely because of their importance to the federally threatened Canada lynx (Lynx canadensis). Understanding the habitat relationships of snowshoe hare populations at the southern periphery of their distribution is particularly important because a warming climate is reducing the mesic forests and persistent snow cover they require. Using fecal pellet density as an index, we examined the factors influencing snowshoe hare relative abundance at 2 study areas in the Southern Rocky Mountains by comparing suites of candidate models containing fine‐scale vegetation and topographical covariates. The Birdseye Gulch study area is within an extensive area containing high elevations, mesic forests, and Canada lynx occurrence. The Three Peaks study area consists of an isolated zone of high elevation and mesic forests surrounded by inadequate habitat and is absent of Canada lynx. Mean hare pellet density in Birdseye Gulch was 2.48 pellets ± 0.46 (SE)/ (n = 49) versus 2.24 ± 0.48/m2 (n = 57) at Three Peaks. Models containing fine‐scale vegetation variables best explained pellet densities at Birdseye Gulch. Pellet densities at Three Peaks were best explained by topographical variables, with much unexplained variation within all models. The differing trends in these areas may be due to the absence of resident Canada lynx at Three Peaks and differences in topography between the areas. Our results indicate that snowshoe hare populations can persist in the type of isolated habitat that is increasingly common at the southern range periphery; however, the use of vegetation management to conserve habitat in these areas may be less effective because of weaker associations with vegetation structure.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".