Using strategically applied grazing to manage invasive alien plants in novel grasslands
Bibliographic record
Abstract
Novel ecosystems that contain new combinations of invasive alien plants (IAPs) present a challenge for managers. Yet, control strategies that focus on the removal of the invasive species and/or restoring historical disturbance regimes often do not provide the best outcome for long-term control of IAPs and the promotion of more desirable plant species. This study seeks to identify the primary drivers of grassland invasion to then inform management practices toward the restoration of native ecosystems. By revisiting both published and unpublished data from experiments and case studies within mainly an Australian context for native grassland management, we show how alternative states models can help to design control strategies to manage undesirable IAPs by manipulating grazing pressure. Ungulate grazing is generally considered antithetical to invasive species management because in many countries where livestock production is a relatively new disturbance to grasslands (such as in Australia and New Zealand as well as Canada and the USA), selective grazing pressure may have facilitated opportunities for IAPs to establish. We find that grazing stock can be used to manipulate species composition in favour of the desirable components in pastures, but whether grazing is rested or strategically applied depends on the management goal, sizes of populations of the IAP and more desirable species, and climatic and edaphic conditions. Based on our findings, we integrated these relationships to develop a testable framework for managing IAPs with strategic grazing that considers both the current state of the plant community and the desired future state—i.e. the application of the principles behind reclamation, rehabilitation, restoration or all three—over time.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".