The impact of fire severity from the 2019 to 2020 mega‐fires on roosting ecology of a rainforest‐dependent bat (<i>Phoniscus papuensis</i>)
Bibliographic record
Abstract
Abstract The 2019–2020 megafires in southeastern Australia extensively burnt forests including fire‐sensitive rainforests. Assessments of species' responses typically consider differences in occupancy or density between burnt and unburnt forest, but here we focus on how these fires influenced roost selection by a rainforest‐dependent bat. We radio‐tracked golden‐tipped bats Phoniscus papuensis in fire grounds to investigate whether roost location or type was influenced by fire severity one‐year post‐fire. Overall, we tracked 19 bats for a total of 117 roost‐days. Bats roosted (including maternity colonies) in the suspended nests of yellow‐throated scrub‐wrens and brown gerygone, typically in gully rainforest. No bats were captured, or roosts located, where fire severity was high. In the burnt portion of the northern study area, P. papuensis displayed a significant preference for roosting in unburnt rainforest compared to its availability along adjacent gullies. Patches of rainforest burnt by low–moderate severity fire were ranked as second preference. In the burnt portion of the southern study area, most roosts were in rainforest mapped as burnt by low–moderate fire severity, however, no selection was evident relative to availability of rainforest and mapped fire severity. Actual roost locations in the southern study area revealed that 62% were in small pockets of unburnt rainforest, with burnt areas nearby. We recorded early breeding and signs of a second litter in late summer, suggesting a post‐fire resource pulse in their prey (spiders). A higher than usual reproductive output may assist in recovery, along with the bat's mobility to aid finding remaining pockets of unburnt or low severity burnt rainforest with suitable bird nests for roosting. However, recolonization of rainforest burnt by high severity fire will first require recovery of structural complexity, microclimate and the recolonization of host bird‐built nests. We recommend ongoing monitoring to assess the recovery of this specialist bat.
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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 | 0.000 | 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".