Impacts of Fire on Snowshoe Hares in Glacier National Park, Montana, USA
Bibliographic record
Abstract
Abstract Forest fires fundamentally shape the habitats available for wildlife. Current predictions for fire under a warming climate suggest larger and more severe fires may occur, thus challenging scientists and managers to understand and predict impacts of fire on focal species, especially species of management concern. Snowshoe hares (Lepus americanusErxleben) are a common and important prey animal in boreal forests and are the primary prey for the US federally threatened Canada lynx (Lynx canadensisKerr), so understanding hare dynamics in post-fire landscapes is critical for managing lynx. We collected habitat and fecal pellet data from 114 sites across three natural burn treatments (mature forest, 1988 Red Bench burn, and 1994 Adair and Howling burns) to evaluate impacts of fire and fire-habitat interactions on snowshoe hare in Glacier National Park, Montana, USA. We found that hare numbers were low throughout the park, with hares absent at 17 % of surveyed sites and occurring at densities above 0.5 hares ha−1(a commonly suggested threshold for supporting Canada lynx) at only 7 % of sites. Hare densities were variable but 10 to 20 times higher in regenerating lodgepole pine (Pinus contortaDouglas ex Loudon) stands of 1988 Red Bench burn compared to lodgepole stands in other burn treatments. In stands dominated by other tree species, we found little difference in hare densities across burn treatments. Regardless of burn history or dominant canopy type, percent canopy cover was positively associated with hare relative abundance. Hare densities also increased with percent understory cover up to 80 % cover, beyond which they began to decline. The regular occurrence of wildfires in Glacier National Park, with 2003 being a particularly large fire year (the largest since 1910), suggest that hare and lynx distribution and abundance within the park may shift substantially in the coming decades as these animals respond to changing spatiotemporal patterns of regenerating forest.
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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 | 0.000 |
| 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".