Soil microbes alter competition between native and invasive plants
Bibliographic record
Abstract
Abstract Invasive plants can alter soil microbial communities and generate positive plant–soil feedbacks that facilitate their performance, but the magnitude and direction of feedbacks may change with novel conditions under climate change. We assessed how potential soil legacy effects of plant invasion and simulated drought influenced plant performance and competition in the longleaf pine ecosystem. We collected soil from a 4‐year factorial invasion (cogongrass Imperata cylindrica ) by drought (simulated with rainout shelters) field experiment and used it as live or sterilized soil inoculum in a greenhouse experiment that included two native foundation species, longleaf pine Pinus palustris and wiregrass Aristida stricta , and cogongrass, grown individually or in competition. There was no evidence of biotic soil legacy effects of invasion or drought for any plant species, but microbes played a significant role in competition. When plants were grown alone, the invader had 12% greater biomass in live soil than sterile soil but both native species had 25% less biomass in live soil. When grown in competition, these effects were reversed for cogongrass (37% smaller in live soil) and pine (17% larger in live soil). In competition, the three species grown in sterile soil produced similar amounts of biomass, whereas live soil created a competitive hierarchy where pine was more competitive than wiregrass and cogongrass. Synthesis . These results emphasize the importance of soil biota in native plant restoration because, although the invader was highly successful when grown alone, plant–microbe interactions influenced the outcome of competition between native and invasive species by promoting native longleaf pine. There was little evidence that invasive cogongrass inhibited native plants via biotic soil legacies, instead, results suggested that plant–soil interactions can be highly resilient to global change such that the biotic legacy of invasion and drought may not promote or inhibit invasion.
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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 | 0.001 |
| 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".