Impact of wildfire size on snowshoe hare relative abundance in southern British Columbia, Canada
Bibliographic record
Abstract
Abstract Background Large wildfires result in more heterogeneous fire scars than do smaller fires because of differences in landscape context and high variability in burn intensity and severity. Previous research on mammal response to wildfire has often considered all fires as comparable disturbances regardless of size. Here, we explicitly examine whether fire size affects relative abundances of a keystone herbivore, snowshoe hare (Lepus americanus Erxleben, 1777), in regenerating stands of the same age. We surveyed vegetation and fecal pellets of snowshoe hares in nine 13-year-old wildfires, specifically, three fires in three size categories—small (80 to 200 ha), medium (1000 to 5000 ha), and large (>10 000 ha)—and in mature forests in southern British Columbia, Canada. Results Snowshoe hare density was low (0.4 hares ha−1), but hares were present at 57% of mature sites. Hares were absent from all areas where small fires had burned and were found in only one medium area post fire (0.2 hares ha−1). Hares were found within the fire scars of all three large burned areas, and with much higher numbers (3.8 hares ha−1) than in the medium fire area or mature forest. Snowshoe hare abundance was highly correlated with the number of sapling trees, especially lodgepole pine (Pinus contorta Douglas ex Loudon). Sapling densities were highly variable, but dense stands of saplings were found only in burn scars from large wildfires. Conclusions Fire size is an important predictor of snowshoe hare relative abundance in areas that are regenerating post fire; fires of different sizes are not comparable disturbances. Specifically, the post-fire heterogeneity after large fires enabled both the highest hare numbers as well as patches with no hares. These results suggest that forest and wildlife managers should protect areas with dense regeneration post fire, as these sites are necessary for hares after large wildfires.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".