Litter nutrient release and allelopathy jointly contribute to the diversity–invasibility relationship
Bibliographic record
Abstract
Abstract Elton's biotic resistance hypothesis suggests that diverse communities are more resistant to biological invasions. While below‐ground mechanisms play a crucial role in regulating the diversity–invasibility relationship, the role of litter as a key regulator of below‐ground processes remains underexplored. We used 15 common native plant species from temperate forests in China to create litter mixtures with five different levels of species richness (1, 3, 6, 9, 15), and the invasive plant Phytolacca americana as a model invader. We investigated how these litter mixtures affected the growth of the invader and explored the roles of nutrient release and allelopathy in this process. Our results demonstrate that increased litter species richness decreased the relative abundance of dominant saprotrophic fungi, resulting in reduced nutrient release and suppressed the growth of P. americana . Additionally, the allelopathic inhibitory effects of litter on the invader intensified as litter species richness increased. These findings elucidate the roles and mechanisms of litter species richness in resisting invasion, demonstrating that the combined effects of nutrient dynamics and allelopathy are major drivers of the diversity–invasibility relationship. We also highlight the potential of incorporating native plant litter into ecosystem management plans to enhance community resistance to invasions, as well as ecosystem stability and sustainability. 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 | 0.001 | 0.001 |
| 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".