Bibliographic record
Abstract
The trajectory of plant invasions - for better or for worse - can be tied to interactions between plants and the soil community. Here, we highlight five broad ways in which belowground interactions can influence the trajectory of biological invasions by non-native plant species. First, many non-native plant species in their non-native ranges can interact very differently with the resident soil community than do native species. Second, non-native plant species often interact very differently with the soil community in their non-native ranges than in their native ranges, which can result in enemy release from antagonistic interactions. Third, non-native plant species can cultivate a soil community that disproportionately harms native competitors in invaded communities. Fourth, antagonistic soil biota in invaded communities can reduce the performance of non-native plant species, resulting in meaningful biotic resistance against invasion. Fifth, besides or in addition to antagonistic interactions with soil biota, soil mutualisms can promote the success of invasive plant species (i) when mutualists co-invade with non-native plant species that require obligate specialist mutualists, (ii) when mutualists enhance the performance of non-native plant species in their non-native ranges, and (iii) when biotic interactions in the invaded community suppress the soil mutualists of native plant species. We conclude that management practices aimed at manipulating plant - soil interactions have considerable potential to help control plant invasions, but further work is needed to understand the spatial, temporal, taxonomic and biogeographic drivers of context dependence in interactions among plants and soil biota.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".