Dead forests burning: the influence of beetle outbreaks on fire severity and legacy structure in sub‐boreal forests
Bibliographic record
Abstract
Abstract Recent regional mountain pine beetle (MPB) outbreaks have generated unprecedented tree mortality across the fire‐prone landscapes of western North American forests and could potentially modify fire severity and postfire ecological effects. In 2012, 2013, and 2014, three fires burned through high mortality, gray‐phase lodgepole pine‐dominated forests in the plateau regions of central interior British Columbia, Canada, providing an opportunity to test for interactions between MPB outbreaks and wildfires. We inventoried 63 plots that spanned gradients of outbreak severity, fire severity, and burning conditions in a wilderness setting. Our objective was to evaluate the influence of outbreak severity on fire severity by assessing typical first‐order fire effects as well as legacy structure related to the consumption of woody biomass on snags/trees. We found no evidence of a relationship between outbreak severity and fire severity for six of seven first‐order fire effects, with the exception of deep charring. We found evidence that legacy structure in the form of consumed branch structure and deep char development had greater odds of occurrence onMPB‐killed snags compared to trees killed during wildfire. Our results indicate two key findings. First, fire severity as it relates to most first‐order fire effects measures is not influenced by outbreak severity, instead it is more strongly influenced by the interaction of fuels, weather, and topography during fire events. Second, our results highlight how the interaction between outbreak severity and fire severity alters postfire structural legacies and their functional attributes, which could have important ecosystem implications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".