Influence of stand-level vegetation and landscape composition on the abundance of snowshoe hares (Lepus americanus) in managed forest stands in western Montana
Bibliographic record
Abstract
Snowshoe hares {Lepus americanus) are a major food source o f the federally threatened Canada lynx {Lynx canadensis).Maintaining a high density of snowshoe hares is therefore critical to lynx conservation.This study examined variation in hare abundances (as indexed by fecal pellet counts) in managed forest stands in two regions o f western Montana.Horizontal cover variables above 0.5 m were most predictive o f hare pellet densities.Understory components were more predictive than other strata of vegetation.A model comprised of variables presently found in National Forest inventories was not as predictive of hare densities as those models containing horizontal cover.Significant differences in hare abundance existed in a variety of managed stand types in western Montana.Unthinned stands had the highest pellet densities, while pole stands had the lowest densities.The relationship between pre-commercial thinning and pellet densities varied with the temporal scale under consideration.Stands that underwent pre commercial thinning 5-10 years ago had significantly lower hare abundances than stands that had not been pre-commercially thinned.Pellet densities increased .astime since thinning increased.The vegetative variables predicting snowshoe hare abundances within these individual stand types differed fi*om each other and from those identified across all stand types, though understory vegetation continued to dominate the models.This study also found a limited number of landscape variables that significantly predicted hare pellet densities.Two variables, perimeter to area ratio and disturbance within a 600 m buffer, significantly predicted pellet densities across all stand types, though their ability to explain variation in pellet density was weak.The relationship of pellet densities to explanatory variables was inconsistent and often contradictory between the two study regions.Integration of stand-level vegetation with landscape metrics resulted in an improved ability to predict hare pellet densities.However, as stand-level vegetation was much more predictive of pellet densities than landscape variables, it is recommended that management focus on individual stand units to optimize the distribution of habitat capable of supporting high densities of snowshoe hares.
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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".