Stable Isotopes Reveal the Drivers of Post‐Wildfire Natural Regeneration of Interior Douglas‐Fir Seedlings in British Columbia
Bibliographic record
Abstract
ABSTRACT Wildfires are increasing in frequency and severity due to climate change, posing challenges to forest ecosystems, including the southern interior of British Columbia, Canada. Interior Douglas‐fir (Pseudotsuga menziesii var. glauca) is a species of great cultural, ecological, and economic importance, necessitating the investigation of post‐wildfire regeneration amidst this changing wildfire regime. This study examines interior Douglas‐fir seedling regeneration across three burn severity levels (low, moderate, high) 5 years post‐wildfire at a site in interior British Columbia. Natural regeneration and seedling traits were measured in 2022 and paired with stable isotope analyses (δ13C, δ15N, δ18O) and foliar nutrient assessments. We employed linear mixed‐effects models to assess the impact of burn severity and light, water, and nutrient factors on seedling biomass. Results indicate higher seedling density in low severity sites but larger individual biomass in moderate and high severity sites. Light availability was the primary factor limiting individual seedling biomass, with greater δ13C and biomass in high severity sites, suggesting that reduced canopy cover enhances photosynthesis and water use efficiency. Despite higher solar exposure, seedlings in high severity sites did not show increased drought stress according to leaf δ18O and stem water contents, likely due to reduced interception and competition for soil water by overstory trees. Biomass growth was not linked to leaf nutrient status, indicating nutrient availability, particularly N, did not limit seedling biomass. While light availability is the current primary growth‐limiting factor for regenerating interior Douglas‐fir seedlings in this study, increased frequency and intensity of heat waves and droughts associated with climate change may increase water stress, emphasizing the need for long‐term monitoring and adaptive management to support the regeneration of interior Douglas‐fir.
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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 | 0.001 | 0.001 |
| 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".