Fire severity has lasting effects on the distribution of arboreal mammals in a resprouting forest
Bibliographic record
Abstract
Abstract Wildfire severity is assumed to be an important driver of habitat availability and species' distributions in forest ecosystems. Many studies have focused on the immediate or short‐term effects of fire severity in fire‐prone ecosystems, with much less focus on how long the effect of fire severity may persist. We examined the effect of fire severity on the distribution of arboreal mammals at 3 and 10 years post‐fire, in a temperate forest ecosystem dominated by eucalypts that have strong resistance and resilience to low and high severity fire, respectively. The study took place within areas affected by the 2009 Kilmore East‐Murrindindi wildfires in south‐eastern Australia. Sites were established across a gradient of fire severity (unburnt through to canopy consumption) and surveyed at 3 and 10 years following fire. Owing to low detection rates for many species, analyses were limited to greater glider (Petauroides volans) occurrence and arboreal species richness. At both 3 and 10 years post‐fire, the greater glider was restricted to unburnt forest or sites affected only by understorey fire, which acted as fire refugia. Greater gliders were absent from sites affected by high severity, canopy‐consuming fires at both points in time. Greater glider occurrence was associated with high levels of canopy cover (>50%), which were typically those areas that were unburnt or burnt at low severity. The richness of arboreal mammals increased over time and was greater in unburnt or minimally affected forest. Our findings emphasize: (i) that fire severity is an important driver of the distribution of arboreal mammals; and (ii) the importance of recognizing longer‐term effects of fire severity in assessing species distributions, even in highly resilient forest communities. Recent advances in mapping fire severity provide new opportunities to assess the effects of fire severity on the distributional patterns of fauna in eucalypt forests.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".