Effects of compound disturbance on Canada lynx and snowshoe hare: Wildfire and forest management influence timing and intensity of use
Bibliographic record
Abstract
Wildfires are increasing in scale and impact on the landscape, altering large amounts of wildlife habitat and forest ecosystems. The reduction of fuels through forest management is considered a primary way to reduce the extent and severity of wildfires before they occur but may lead to a decrease in tree density prohibitive of some species’ habitat. Alternatively, management actions undertaken after a fire may speed the trajectory of burned areas back into quality habitat but may also impede this development if the wrong type of treatment is undertaken. Thus, information on how different management actions, applied either pre- or post-fire, can influence the timing of a burned area’s return to suitable habitat will help managers conserve species on the landscape. Our study aims to understand how a rare carnivore, Canada lynx ( Lynx canadensis ), uses stands managed with different silviculture actions at different times relative to wildfire. We used GPS locations from 39 individual lynx collected from 2004 to 2015 to examine the response of lynx to wildfire compounded by active forest management, where time since fire at time of use ranged from 1 to 27 years. To understand the drivers behind lynx use of wildfires, we also focused on the primary prey of Canada lynx, snowshoe hares ( Lepus americanus ), using pellet counts across a similar range of post-fire treatment types in fires between 22 and 28 years old. We also assessed vegetation recovery and forest structure over time since wildfire using remotely sensed data and field measurements. We found that lynx intensity of use differed based on timing and type of management action, with the greatest lynx use ∼25 years after a wildfire managed with post-fire regeneration cuts (removal of the majority of the canopy). Lynx use was likely driven by hare abundance, which was also highest in post-fire regeneration cuts, characterized at time of use by dense lodgepole pine stands. We conclude that managing landscapes with a mosaic of active (pre- and post-fire treatments) and passive (hands-off) management will best conserve a desirable range of lynx habitat in an increasingly fire-impacted landscape.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".