Landscape ecology of house mouse outbreaks in south‐eastern Australia
Bibliographic record
Abstract
Summary House mouse Mus domesticus outbreaks in the grain‐growing areas of south‐eastern Australia occur irregularly and may be local or widespread, covering thousands of square kilometres. All natural and agricultural habitats are occupied when house mouse numbers are high, and the question we addressed was whether we can distinguish source and sink habitats within these agricultural landscapes so that management practices can be better targeted. Live‐trapping on replicated grids in 15 habitats, including eucalypt woodland, cypress pine woodland, areas of permanent water and crop habitats, was carried out from 1983 to 1988 at 9‐week intervals. Agricultural cropland (including crops, fallow and pastures), farm buildings, seepage areas and natural woodland could be source habitats. Farm buildings, seepage areas and saltbush areas all had high mouse densities entering the 1983–84 outbreak and were refuge habitats for mice. Cropland habitats quickly became the source area in spring 1983, and woodlands were initially sinks that lagged 2–4 months behind the population growth shown in crops. Adult female mice in cropland habitats were more often in breeding condition compared with mice in natural woodland. Mice also had higher indices of residency in cropland than in natural woodland. Synthesis and applications . In non‐irrigated cereal production areas of south‐eastern Australia, house mice move from refuge habitats in seepage areas and farm buildings into crops, build up in numbers in cropland habitats, and then invade woodland habitats, which by themselves cannot generate outbreaks of this pest species. Monitoring for incipient outbreaks should concentrate on refuges in seepage areas and crops and their associated fence lines as source habitat indicators. Population control in these habitats in spring would reduce the likelihood of population outbreaks in autumn, leading to reduced rodenticide use and a concomitant reduction in environmental hazards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.002 | 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 teacher head, 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".