Small mammals and mesomammals in a post-fire and salvage-logged landscape
Bibliographic record
Abstract
Wildfire influences the ecosystem function of fire adapted ecosystems. Climate change has shifted the natural fire regime of North American forests to tend to larger, higher severity fires. While wildfire is a natural disturbance, it is detrimental to the forestry industry. Salvage logging is a common practice in British Columbia (BC), Canada, forests following wildfire, and it often differs from standard harvesting practices in that it removes timber from larger expanses of land. The impacts of post-fire salvage logging on wildlife are poorly understood and may compromise the ecosystem function of post-fire landscapes. To better understand the impacts of wildfire and post-fire salvage logging on forest ecosystems, I determined the population responses of deermice (Peromyscus maniculatus), southern red-backed voles (Myodes gapperi), snowshoe hares (Lepus americanus), and red squirrels (Tamiasciurus hudsonicus). I studied these important species in a post-fire and salvage-logged interior Douglas fir (Pseudotsuga menziesii) forest on the Chilcotin Plateau, BC. I live-trapped small mammals to estimate their abundances in mature, post-fire, and post-fire salvage-logged habitats. Post-fire regenerating forest supported the highest abundances of southern red-backed voles in the study area. However, post-fire salvage logging reduced southern red-backed vole populations and supported mainly deermice, a generalist species. Snowshoe hare densities were lowest in post-fire salvage-logged sites. These sites supported significantly fewer hares than regenerating post-fire stands that burned eight-nine years ago or mature forest. Early seral post-fire regenerating stands supported the highest densities of snowshoe hares. Red squirrel densities were highest in mature forest sites, but squirrels also used eight to nine-year-old post-fire regenerating sites. Post-fire salvage logging significantly changed vegetation structure by reducing tree basal area, rendering sites unsuitable for snowshoe hares, red squirrels, and southern red-backed voles. Post-fire salvage logging delayed the recolonization of burned areas by voles, snowshoe hares, and red squirrels and removed valuable regenerating forests from the landscape. Given the increasing size of wildfires and the scale of post-fire salvage logging, the decreases in abundances of small mammal species caused by post-fire salvage logging could impact the health of forest ecosystems across entire 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 | 0.000 | 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) | 0.001 | 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".