Drought, topography, and forest management shape wildfire occurrence and severity in montane Australian landscapes
Bibliographic record
Abstract
In recent decades, changes in climate and land use have reshaped forest landscapes across the globe, altering the timing and severity of forest fires. This study investigated the influence of climate, landform, forest disturbances, and management on wildfire occurrence and severity in the montane forests of south-eastern Australia. Modelling of spatial data from 1981 to 2020 showed that fire occurrence was highly sensitive to the top-down influence of antecedent drought, whereas fire severity was primarily influenced by bottom-up factors such as topography, past fires, and historical timber harvesting. Below-average rainfall and high vapour pressure deficit, incorporated into a forest drought stress index, were postiviely associated with wildfire occurrence. This relationship between climate and wildfire occurrence varied across ecosystems and was generally stronger in lower elevation montane forests compared to higher elevation areas. Time since the last fire was also influential: forests that had recently burned (e.g., <5 years) or had no history of fire were less likely to burn or experience high severity wildfire. Clearfell harvesting reduced the probability of fire occurrence and high severity fire for several decades post-harvest relative to forests with no recorded harvesting. Topography modulated landscape flammability: fire occurrence and severity increased with steeper slopes and were reduced in moist topographic locations. These findings highlight that climate, landform, and land use are important drivers of fire occurrence and severity in montane forests. Strategic use of prescribed burning, protection of topographic refugia, and assisted forest regeneration could help maintain the structure and composition of montane forests in a more flammable future.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".