Cheatgrass invasion alters the abundance and composition of dark septate fungal communities in sagebrush steppe
Bibliographic record
Abstract
Invasive, non-native plant species can alter soil microbial communities in ways that contribute to their persistence. While most studies emphasize mycorrhizal fungi, invasive plants also may influence communities of dark septate fungi (DSF), which are common root endophytes that can function like mycorrhizas. We tested the hypothesis that a widespread invasive plant in the western United States, cheatgrass (Bromus tectorum L.), influenced the abundance and community composition of DSF by examining the roots and rhizosphere soils of cheatgrass and two native plant species in cheatgrass-invaded and noninvaded areas of sagebrush steppe. We focused on cheatgrass because it is negatively affected by mycorrhizal fungi and colonized by DSF. We found that DSF root colonization and operational taxonomic unit (OTU) richness were significantly higher in sagebrush (Artemisia tridentata Nutt.) and rice grass (Achnatherum hymenoides (Roem. & Schult.) Barkworth) from invaded areas than noninvaded areas. Cheatgrass roots had similar levels of DSF colonization and OTU richness as native plants. The community composition of DSF varied with invasion in the roots and soils of native species and among the roots of the three plant species in the invaded areas. The substantial changes in DSF we observed following cheatgrass invasion argue for comparative studies of DSF function in native and non-native plant species.
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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 | 0.000 |
| 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) | 0.000 | 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".