Disturbance-dependent invasion of the woody weed, Calotropis procera, in Australian rangelands
Bibliographic record
Abstract
Plant invasions are threats to biodiversity and ecosystem processes that have far reaching ecological and economic impacts. Understanding the mechanisms of invasion essentially helps in developing effective management strategies. Rubber bush (Calotropis procera) is an introduced milkweed that invades Australian beef production rangelands. Its establishment is often associated with disturbances caused by pastoral management practices. We examined whether or not rubber bush (1) outcompetes native grasses, (2) can invade intact rangeland, and (3) if disturbance facilitates rubber bush establishment and spread in grassy rangelands. We measured the competitive response of different densities of Mitchell grass (Astrebla pectinata) individuals and the competitive effects of associate rubber bush seedlings in an additive common garden experiment. Replicated field exclosure experiments, under grass-dominated and tropical savanna woodland conditions examined the effect of increasing levels of disturbance on rubber bush seedling emergence. The dominant native Mitchell grass was a stronger competitor than rubber bush when grown together under greenhouse conditions, whereby root and shoot biomass yields were more restricted in rubber bush compared with Mitchell grass. This finding was corroborated in the field exclosure experiments at both sites, where seedling emergence increased 5-fold in seeded and highly disturbed plots where superficial soils were turned over by treatments simulating heavy grazing and trampling by cattle or machinery. Emergence of rubber bush seedlings in seeded plots that were undisturbed, clipped and grazed was minimal and rubber bush seedlings did not survive the seedling stage in these plots. These results demonstrate that disturbance to the superficial soil stratum affects the ability of rubber bush seeds to successfully establish in a microsite, and high levels of soil disturbance substantially increase establishment. Thus, rubber bush is a poor competitor of Mitchell grass and does not invade intact grassland. Consequently, rubber bush invasion is disturbance-dependent in the vast Australian rangelands. The spread of this weed may be arrested by management practices that minimise disturbances to grass cover.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".