The REBURN model: simulating system-level forest succession and wildfire dynamics
Bibliographic record
Abstract
Abstract Background Historically, reburn dynamics from cultural and lightning ignitions were central to the ecology of fire in the western United States (wUS), whereby past fire effects limited future fire growth and severity. Over millennia, reburns created heterogenous patchworks of vegetation and fuels that provided avenues and impediments to the flow of future fires, and feedbacks to future fire event sizes and their severity patterns. These dynamics have been significantly altered after more than a century of settler colonization, fire exclusion, and past forest management, now compounded by rapid climatic warming. Under climate change, the area impacted by large and severe wildfires will likely increase — with further implications for self-regulating properties of affected systems. An in-depth understanding of the ecology of reburns and their influence on system-level dynamics provides a baseline for understanding current and future landscape fire-vegetation interactions. Results Here, we present a detailed characterization of REBURN — a geospatial modeling framework designed to simulate reburn dynamics over large areas and long time frames. We interpret fire-vegetation dynamics for a large testbed landscape in eastern Washington State, USA. The landscape is comprised of common temperate forest and nonforest vegetation types distributed along broad topo-edaphic gradients. Each pixel in a vegetation type is represented by a pathway group (PWG), which assigns a specific state-transition model (STM) based on that pixel’s biophysical setting. STMs represent daily simulated and annually summarized vegetation and fuel succession, and wildfire effects on forest and nonforest succession. Wildfire dynamics are driven by annual ignitions, fire weather and topographic conditions, and annual vegetation and fuel successional states of burned and unburned pixels. Conclusions Our simulation study is the first to evaluate how fire exclusion and forest management altered the active fire regime of this landscape, its surface and canopy fuel patterns, forest and nonforest structural conditions, and the dynamics of forest reburning. The REBURN framework is now being used in related studies to evaluate future climate change scenarios and compare the efficacy of fire and fuel management strategies that either enable the return of active fire regimes or depend on fire suppression and wildfire effects on forest burning.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".