Nutrient availability and invader density regulate the diversity–invasibility relationship mediated by soil microbes
Bibliographic record
Abstract
Abstract Elton's biotic resistance hypothesis, which proposes that diverse plant communities are more resistant to biological invasions, has received considerable experimental support. However, the roles of soil microbes in the diversity–invasibility relationship and how they are influenced by nutrient availability and invader density remain poorly understood in temperate forest ecosystems. We conditioned soils for nearly 1 year using nine individually grown native woody species. We then mixed the soils to create a synthetic diversity gradient of one, three or six species. The invasive plant Phytolacca americana was then grown in pots inoculated with these differently conditioned soils. Each soil treatment was crossed with two nutrient availabilities (fertilised vs. non‐fertilised) and three invader densities (one, two and four plants) to test invasion success. We found that soil treatments did not significantly affect P. americana biomass under low nutrient availability, whereas biomass increased with increasing soil inoculum diversity under high nutrient availability. Soil treatments had no significant effect at invader densities of two and four, whereas P. americana biomass increased with increasing diversity levels at a density of one. Furthermore, among all native species, Quercus acutissima reduced P. americana biomass at an invader density of four, although this suppressive effect disappeared with nutrient addition. Analysis of the soil microbial community suggests that these patterns may reflect diversity‐driven pathogen dilution and a reduction in the relative abundance of saprotrophic fungi associated with Q. acutissima . Overall, our results indicate that the diversity–invasibility relationship is modulated by soil microbes, and this modulation depends on nutrient availability and invader density. By integrating the interactive effects of plant diversity, nutrient conditions and invasion intensity, this study provides a potential microbial pathway to explain context‐dependent shifts in biotic resistance. Furthermore, our findings also highlight the important role that some specific native species can play in enhancing community resistance to invasion in particular ecological contexts. Read the free Plain Language Summary for this article on the Journal blog.
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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".