Wildfire and spruce beetle outbreak: Simulation of interacting disturbances in the central Rocky Mountains
Bibliographic record
Abstract
Infrequent large-scale natural disturbance regimes are an integral component of Engelmann spruce (Picea engelmannii) forests of the central Rocky Mountains. Wildfires, bark beetle outbreaks, winds, and avalanches cause relatively drastic changes in community structure, composition, and function. These disturbances may occur independently or interact where the incidence of one may change the potential for another. We assessed potential wildfire behaviour change in the wake of a catastrophic, landscape-wide spruce beetle (Dendroctonus rufipennis) outbreak in southern Utah, USA. Using data collected in spruce forests affected by the outbreak, the Forest Vegetation Simulator and Fire and Fuels Extension were used to simulate long-term (100 y) stand dynamics and potential fire behaviour under 3 reconstructed scenarios: no spruce beetle outbreak (low-severity), 50% spruce beetle-caused mortality (mid-severity), and 95% spruce beetle-caused mortality (high-severity). Simulations suggested a likely reduction in probability of active crown fire for 1 or 2 decades on near-pure Engelmann spruce sites after high-severity mortality. This counterintuitive result suggested extreme fire behaviour is not an inevitable consequence of spruce beetle outbreaks. No change in potential fire behaviour was predicted in stands with the least reduction in spruce basal area (low- or mid-severity). In one stand with a history of surface fire, stand structure and potential fire behaviour from low- and high-severity simulations were influenced by surface fire ˜100 y ago. These results are indicative of complex disturbance interactions that were influenced by the host-specific spruce beetle, resultant stand structures and fuel profiles, and in one case antecedent disturbance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".