Effects of burn severity and postfire salvage logging on carnivore communities in montane forests
Bibliographic record
Abstract
Abstract Wildfire and postfire salvage logging are major drivers of forest succession in western North America. Although postfire landscapes support a variety of carnivore species, it is unclear how these animals respond to differing patterns and severities of burning, or to additional landscape change from postfire salvage logging. Open, early-seral habitats created by these disturbances are predicted to benefit generalists such as coyotes (Canis latrans) and weasels (Mustela spp.), but restrict the activity of forest specialists such as Canadaian Lynx (Lynx canadensis) and Pacific Marten (Martes caurina). We used winter track surveys, supplemented with remote cameras, to examine carnivore habitat use in and around large, mixed-severity burns in north-central Washington, United States (burned in 2006), and central British Columbia, Canada (burned in 2010, then partially salvage-logged; some areas reburned in 2017). At 10 to 13 years postfire, marten had similar detection rates across lightly and severely burned areas of the 2006 burn, as did coyotes. Marten selected lightly burned areas of the 2010 burn (6 to 9 years postfire) over higher burn severities, and selected burns over adjacent unburned forests. Weasels selected areas of higher burn severity on both landscapes, while Lynx selected lower burn severities. Weasels and coyotes selected areas with a greater proportion of salvage-logged habitat in the 2010 burn, while marten, lynx, and wolves (Canis lupus) avoided areas with salvage logging. Fishers (Pekania pennanti) showed no clear patterns of selection or avoidance in relation to burn severity or salvage logging. Burn severity strongly influences wildlife activity postfire; lightly burned residual habitats are critical for forest specialists. Postfire salvage logging alters carnivore communities and may hinder species that require structurally complex landscapes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".