Fire severity and its local extent are key to assessing impacts of Australian mega‐fires on koala (<i>Phascolarctos cinereus</i>) density
Bibliographic record
Abstract
Abstract Aim Mega‐fires are predicted to increase with climate change. Australia experienced the largest ever documented forest fires in 2019–2020, but the response of most taxa remains poorly known. We used acoustic arrays to estimate impact of the mega‐fires on the density of an iconic marsupial. Location North‐east New South Wales, Australia. Time period Pre‐fire density estimated in 2018–2019 and post‐fire density in 2019–2020. Major taxa studied Koala, Phascolarctos cinereus. Methods We estimated male density before and after fires using large acoustic arrays and spatial count models. Acoustic arrays sampled three timber production forests with a gradient in fire severity and three unburnt controls in national parks. Results Koalas were temporarily extirpated where high fire severity dominated the landscape, but some localized recovery was evident after 1 year. Where moderate severity fire dominated, density was reduced by about 50% within 1 year, but koalas were widespread throughout the burnt area. In our third area dominated by low severity fire, no impact was detected as pre‐ and post‐fire uncertainty intervals overlapped. Control sites surveyed at similar times showed little change in density between years. There was no relationship between pyrodiversity and koala density. Within arrays broadly dominated by moderate or high severity fire, density 1 year after fire was lower in burnt patches of both low and high severity fire. Regionally, 9.8% of landscape samples (2 km × 2 km) in koala habitat were dominated by high fire severity, while a further 6.1% were dominated by moderate fire severity, suggesting c. 13% decline in koala density. Main conclusions A substantial impact of high severity fire was confirmed. Severe impacts were localized across the landscape, recovery had begun within a year and resilience was evident where low severity fire dominated. However, more frequent fires in the future will compound koala losses.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".