Compositional profiling of the rhizosphere microbiome of Canada thistle reveals consistent patterns across the United States northern Great Plains
Bibliographic record
Abstract
Canada thistle is a pervasive perennial weed, causing challenges to agricultural and natural ecosystems globally. Although research has focused on the phenology, genetics, and control of Canada thistle, little is known about the rhizosphere microbiome and the role plant-microbe interactions play in invasion success. This study investigated the rhizosphere microbiome of Canada thistle across diverse climates, soils, and crops in the U.S. northern Great Plains. Soil and rhizosphere samples were collected and bacterial 16S and fungal ITS2 sequencing were performed to characterize the core microbiome and identify potential factors contributing to invasion success. Amplicon sequencing revealed a stable core microbiome that was detected in the Canada thistle rhizosphere across all locations. The core microbiome was dominated by the bacterial phyla Actinobacteriota and Proteobacteria and fungal phyla Ascomycota and Basidiomycota. Differential abundance analysis showed rhizosphere fungal communities were enriched in pathogen-containing genera with a 1.7-fold greater abundance of Fusaria and a 2.6-fold greater abundance of Gibberella compared to bulk soil. Predictive functional profiling showed rhizosphere communities were enriched (p < 0.05, FDR corrected) in plant pathogen fungal guilds which represented 19% of the fungal community. The rhizosphere microbiome was similar in composition across environments, highlighting the stable association between Canada thistle and specific microbial taxa. This study characterized the core microbiome of Canada thistle, and the findings highlight plant-microbe interactions shaping invasive behavior. These findings are important for understanding the ecological impacts of plant invasion and soil-microbe ecological processes.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".