Owl Habitat Use and Diets After Fire and Salvage Logging
Bibliographic record
Abstract
Megafires are transforming western boreal forests, and many burned forests are salvage logged, removing more structure from landscapes and delaying forest regeneration. We studied forest-dwelling owls in a post-fire and salvage-logged landscape in central British Columbia, Canada, in 2018–2019 after the 2010 Meldrum Creek Fire and the 2017 Hanceville Fire. We examined owl habitat selection via call surveys compared to the habitats available in this landscape. Owl pellets were dissected to determine owl diets. We detected six owl species, of which Northern Saw-whet Owls (Aegolius acadicus) were the most common. Owls had weak and variable habitat selection within an 800 m radius of detections; all species used some burned area. Great Gray Owls (Strix nebulosa) and Great Horned Owls (Bubo virginanus) obtained more prey from mature forests (e.g., red-backed voles, Myodes gapperi, snowshoe hares, Lepus americanus) than other owls did, whereas other owls primarily consumed small mammals that were common in burned or salvaged areas. These results indicate a diverse community of owls can use landscapes within a decade after wildfire, potentially with some prey switching to take advantage of prey that use disturbed habitats. Despite that, owl numbers were low and some owls consumed prey that were not available in salvage-logged areas, suggesting that impacts on owls were more severe from the combination of fire and salvage logging than from fire alone.
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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 | 0.001 | 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".