Burn severity modifies the impact of salvage logging on post-wildfire natural regeneration of Douglas-fir in interior British Columbia
Bibliographic record
Abstract
1. Exacerbated by climate change, wildfires in British Columbia, Canada, have increased in extent and severity, impacting forests, including commercially valuable species like interior Douglas-fir ( Pseudotsuga menziesii var. glauca ). Post-wildfire salvage logging aims to mitigate financial losses and accelerate regeneration, though its ecological impacts remain uncertain. 2. This study was conducted in the Alex Fraser Research Forest, where a 2017 wildfire burned approximately 1000 ha. Combined with 2023 seedling biomass and %N measurements, we used linear mixed-effects models to examine the physiological responses of regenerating interior Douglas-fir seedlings to burn severity and salvage logging, using carbon, nitrogen, and oxygen stable isotope analyses (δ 13 C, δ 15 N, and δ 18 O) to assess water-use efficiency (WUE i ), photosynthesis, water stress, and nitrogen cycling post-disturbance. 3. Higher seedling biomass was found in high-severity, not-salvaged sites. Moderate-severity, not-salvaged sites had lower δ 13 C and δ 18 O values compared to high-severity (salvaged and not-salvaged) and moderates-severity, salvaged sites. Higher leaf %N was positively correlated with δ 13 C values across treatments, indicating enhanced water-use efficiency. 4. The statistically significant interactions between burn severity and salvage logging and their influence on seedling biomass, δ 13 C, and δ 18 O emphasize the key role of microclimatic conditions in post-fire recovery. In high-severity sites, salvage logging did not enhance seedling biomass, likely due to already sufficient light availability. In moderate-severity sites, salvage logging had small, positive effects on seedling biomass that were not statistically significant. Higher leaf nitrogen content appeared to boost WUE i across treatments. These findings support tailoring post-wildfire management to burn severity, with minimal intervention in high-severity areas and selective salvage in moderate-severity sites. • Douglas-fir seedling recovery varies with burn severity and salvage logging. • Salvage logging lowers biomass in high severity sites with already sufficient light. • Water stress increases with severity and salvage, yet biomass remains high. • Higher %N is linked to improved water-use efficiency in seedlings. • Findings support adaptive, severity-based post-wildfire management.
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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".