Suppression of soil microbiota rather than neighbours facilitates absinthe (Artemisia absinthium) invasion in native grasslands
Bibliographic record
Abstract
Many mechanisms can lead to successful plant invasion, but their importance is often context dependent. One such mechanism is allelopathy: chemical inhibition of neighbouring plants. The importance of allelopathy may be mediated by soil microbiota and environmental conditions, and depend upon the species or functional group affected. To better understand how these factors alter allelopathy and competition, we conducted a combined field and greenhouse experiment focusing on absinthe (Artemisia absinthium). We experimentally introduced absinthe into native grassland via disturbances and collected soil from beneath these plants after three years. We then tested how invaded versus uninvaded field soil, within burned or unburned grassland, affected the performance of absinthe, two forbs (Achillea millefolium and Medicago sativa), and two grasses (Bromus inermis and Elymus lanceolatus) in a greenhouse competition experiment. We included activated carbon and soil sterilization treatments to test for allelopathic and microbial effects. Microbial inhibition, rather than allelopathy, drove competitive interactions. In uninvaded, unburned soil all species were inhibited and absinthe had no competitive effect. However, when absinthe was released from microbial inhibition by soil sterilization or prescribed fire, it suppressed other species. Microbial inhibition was also reduced for all species in invaded soil, suggesting suppression of pathogens by absinthe. Responses also varied between functional groups, with grasses being more vulnerable to microbial inhibition. Our findings highlight the complexity of invasion effects on soils and the potential for feedbacks on invasion outcomes. They also suggest that disturbance may facilitate invasion via effects on both plants and soil microbes.
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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 | 0.000 | 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".