Pre-fire forest conditions and fire severity as determinants of the quality of burned forests for deadwood-dependent species: the case of the black-backed woodpecker
Bibliographic record
Abstract
Burned forests represent high-quality habitats for many deadwood-dependent species. Yet, post-fire conditions may vary greatly within and among burns and thereby may affect habitat suitability for these species. We studied habitat selection of nesting black-backed woodpeckers ( Picoides arcticus Swainson) in recently burned spruce-dominated boreal forests. Our objectives were to (i) identify factors involved in snag selection for both nesting and foraging and (ii) examine selection of nest sites within the burned landscape. A total of 92 nests and 1612 foraging observations were used to investigate snag selection. Our results show that both pre-fire forest conditions and fire severity are important in determining the quality of burned forests for black-backed woodpeckers. This species selected large snags for both nesting (>20 cm DBH) and foraging (>15 cm DBH). Woodpeckers selected deciduous and degraded “pre-fire” snags for nesting whereas black spruce snags that had been created by fire and that were moderately burned were preferred for foraging. Nest sites were concentrated in burned mature stands and supported higher densities of large snags (e.g., >15 cm DBH). Our results suggest that burned forest patches of at least 20 ha and composed mainly of burned mature and old-growth forests should be maintained during post-fire harvesting. The decrease in the amount of late seral stands in managed forest landscapes raises concerns about the future availability of high-quality burned forests for this species.
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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 | 0.000 | 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".