Effects of fire on grass‐layer savanna macroinvertebrates as key food resources for insectivorous vertebrates in northern Australia
Bibliographic record
Abstract
Abstract This paper documents the effects of fire on grass‐layer invertebrates in tropical savannas of the Kimberley region of north‐western Australia, in the context of resource availability for consumers. Inappropriate fire regimes have been identified as a factor threatening a number of vertebrate groups, including small mammals, across northern Australia, and a possible mechanism might be through the effects of individual fires or fire regimes on food availability. We test for a fire effect on grass‐layer invertebrate resources, which may affect insectivorous savanna vertebrates. Wet season sweep‐net invertebrate samples were taken in two tropical savanna habitats, with contrasting laterite and sandstone substrates, in 2008, 2009 and 2010. Sites were stratified by post‐fire interval to analyse invertebrate successional change after fire. In addition, experimental burns were implemented in 2010 to investigate immediate post‐fire invertebrate responses. Total invertebrate numbers declined by 80–90% immediately (1 week) following fire, reflecting the loss of grass‐layer habitat. Of the commonly sampled invertebrate groups, Araneae, Coleoptera, Hempitera, Lepidoptera, Formicidae and Diptera were all reduced in numbers immediately post‐fire, whereas Orthoptera showed no immediate post‐fire decline. Invertebrate numbers were rapidly restored to pre‐fire levels by the first wet season after fire, and no detectible change was observed in numbers or composition from 1 to 3–4 years post‐fire (the longest post‐fire interval available). This suggests that the effects of individual fires on grass‐layer invertebrates are very short‐lived. Such short‐lived post‐fire responses among grass‐layer invertebrates, plus evidence that most ground‐layer invertebrate groups are fire‐resilient, suggest that food resource limitation is not a tenable explanation for fire‐related declines among insectivorous savanna vertebrates. However, wet season burning could result in significant invertebrate resource depletion in highly flammable habitats (Triodia and Sorghum spp. savannas) during important vertebrate breeding/recruitment periods, if burning is extensive and if grass‐layer invertebrates do not recover within a few weeks of fire.
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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".