Archaea and bacteria mediate the effects of native species root loss on fungi during plant invasion
Bibliographic record
Abstract
Although invasive plants can drive ecosystem change, little is known about the directional nature of belowground interactions between invasive plants, native roots, bacteria, archaea and fungi. We used detailed bioinformatics and a recently developed root assay on soils collected in fescue grassland along a gradient of smooth brome (Bromus inermis Leyss) invasion to examine the links between smooth brome shoot litter and root, archaea, bacteria and fungal communities. We examined (1) aboveground versus belowground influences of smooth brome on soil microbial communities, (2) the importance of direct versus microbe-mediated impacts of plants on soil fungal communities, and (3) the web of roots, shoots, archaea, bacteria and fungi interactions across the A and B soil horizons in invaded and non-invaded sites. Archaea and bacteria influenced fungal composition, but not vice versa, as indicated by redundancy analyses. Co-inertia analyses suggested that bacterial-fungal variance was driven primarily by 12 bacterial operational taxonomic units (OTUs). Brome increased bacterial diversity via smooth brome litter in the A horizon and roots in the B horizon, which then reduced fungal diversity. Archaea increased abundance of several bacterial OTUs, and the key bacterial OTUs mediated changes in the fungi's response to invasion. Overall, native root diversity loss and bacterial mediation were more important drivers of fungal composition than were the direct effects of increases in smooth brome. Critically, native plant species displacement and root loss appeared to be the most important driver of fungal composition during invasion. This causal web likely gives rise to the plant-fungi feedbacks, which are an essential factor determining plant diversity in invaded grassland ecosystems.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".