Integrated management of Canada thistle (<i>Cirsium arvense</i>) in the Great Plains and Intermountain West using a biocontrol agent (<i>Puccinia suaveolens</i>)
Bibliographic record
Abstract
Abstract Canada thistle [ Cirsium arvense (L.) Scop.] is an invasive perennial plant that threatens agricultural landscapes and natural ecosystems worldwide. The extensive regenerative root system of C. arvense complicates control efforts, with current strategies having limited success. Puccinia suaveolens (syn.: P. punctiformis ), an obligate biotrophic rust fungus, has shown potential as a biological control agent by systemically infecting the root system, reducing root mass and shoot growth, and limiting vegetative regeneration; however, its efficacy when integrated with other control methods remains unclear. We conducted experiments from 2020 to 2022 at two sites in Colorado and Utah to evaluate P. suaveolens efficacy when applied alone and in combination with mowing, tillage, and herbicide. Treatments were applied in fall (2020 and 2021), with monitoring of C. arvense stem density and vegetative cover, as well as P. suaveolens incidence before and after treatments through 2022. While P. suaveolens alone contributed to a decrease in C. arvense density, it was far less effective compared with herbicide treatments, and its impact when integrated with mowing or tillage was inconsistent. Herbicide application (alone and when combined with P. suaveolens ) generated the greatest immediate reduction in C. arvense stem density and vegetative cover, although it resulted in the greatest amount of bare ground exposure. Grass coverage present within plots varied significantly between treatments, ranging from 0% to 75%, with the highest percentage observed in herbicide treatments in both years. Forb cover remained below 30% across treatments and years. Although P. suaveolens can contribute to C. arvense suppression, additional research is needed to remove barriers to its successful establishment, systemic infection, and spread within populations, which could improve its efficacy and optimization when integrated with other control strategies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".