Relationship Between Fecal Pellet Counts and Snowshoe Hare Density in Western Wyoming
Bibliographic record
Abstract
ABSTRACT Snowshoe hares ( Lepus americanus ) are an important prey species for Canada lynx ( Lynx canadensis ) and are considered critical for lynx population persistence. Determination of snowshoe hare distribution and abundance is needed by land management agencies for lynx conservation. An accepted approach for estimating snowshoe hare abundance is the use of fecal‐pellet plot counts. Locally derived regression equations are preferred for accurate calibration of pellet counts to snowshoe hare density due to local differences in pellet deposition and decomposition. We used linear regression to examine correlations between snowshoe hare density, as determined by mark–recapture estimates, and pellet plot counts on both uncleared plots and annually cleared plots on the Bridger‐Teton National Forest, western Wyoming, USA. We found significant correlations between snowshoe hare density estimates and fecal pellet counts for both uncleared and annually cleared pellet counts; however, the relationship was stronger (higher r ) when using pellet counts from annually cleared plots. In addition, we found that adjusting the buffer size by omitting hard habitat edges (not used by hares) around trapping grids improved correlations between snowshoe hare density and fecal pellet counts for both uncleared plots and annually cleared plots. Though precision is sacrificed when using uncleared plots, they may be useful as a coarse index of habitat use by snowshoe hares. Our derived regression equations may be useful to identify important foraging habitat for Canada lynx in western Wyoming. Land managers responsible for conserving snowshoe hare habitat in western Wyoming may use these equations to monitor changes in hare populations among habitats and during prescribed management actions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".