Landscape ecology of the burrowing bettong: fire and marsupial biocontrol of shrubs in semi-arid Australia
Bibliographic record
Abstract
Prior to European settlement, medium-sized marsupials, especially bettongs (Bettongia spp.), were widely distributed across arid and semi-arid Australia. Most disappeared rapidly in the late 1800s in the earliest settled rangelands such as the West Darling region of western New South Wales following the spread of domestic herbivores, rabbit invasion, exotic predators and loss of habitat. Because the burrowing bettong (Bettongia lesueur) is the only fossorial macropod species, it left a clearly visible record of its past presence, distribution and habitat preferences in the form of substantial relict warrens, particularly in stony, ‘hard-red’ habitats. With the reduction in fire frequency because of excessive grazing pressures following European settlement in the 19th century, there was a rapid increase in the density of unpalatable native shrubs. We examine the hypothesis that periodic wildfires and browsing by bettongs were together able to regulate shrub densities in semi-arid rangelands in Australia. Information from various sources concerning the effects of fire, rainfall and browsing on the demography of shrubs was used to construct a model of shrub population dynamics. The model indicates the potential for two states for a given bettong density: first, a low shrub density maintained by a combination of periodic fire and bettong browsing; and second, a high shrub density in the absence of fire. These results have broad implications for pastoral and conservation management in Australian semi-arid rangelands.
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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".