Spatial Estimates of Future Fire Risk Considering Climate and Fuel Management for Conservation Planning
Bibliographic record
Abstract
A key gap in conservation management is understanding how fire regimes may shift under climate change and how these shifts might impact biodiversity. Conserving species and communities in the future will require the strategic prioritisation of conservation actions that account for shifting fire regimes. We used a landscape fire regime model, the Fire Regime and Operations Simulation Tool (FROST), to estimate the wildfire risk of 12 regions in Victoria, Australia. Each region is approximately 1.2 million hectares in size and collectively span a range of climatic gradients. We modelled three epochs of climate data: 1990 to 2009, 2020 to 2039, and 2060 to 2079, alongside three fuel management strategies: no prescribed burning, low rates of prescribed burning, and high rates of prescribed burning. We analyse changes in fire frequency, extent, intensity, and severity across Victoria to provide estimates of potential risk under the three management scenarios for each epoch. Wildfire risks increased under future climate predictions and from west to central Victoria, declining again in the eastern regions. These simulations provide baseline estimates for the spatial distribution of future wildfire risk across Victoria, Australia, and can be used to help prioritise conservation actions to areas of the lowest risk. We also found that there were no statistically significant differences between fuel management scenarios, reiterating that prescribed burning will not necessarily negate the impacts of climate change on future wildfire risk. Incorporating spatial estimates of future wildfire risk can improve the prioritisation of conservation decisions and can help protect biodiversity in the long term.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".