A review of the influence of root-associating fungi and root exudates on the success of invasive plants
Bibliographic record
Abstract
Plant-fungal interactions are essential for understanding the distribution and abundance of plants species. Recently, arbuscular mycorrhizal fungal (AMF) partners of non-indigenous invasive plants have been hypothesized to be a critical factor influencing the invasion processes. AMF are known to improve nutrient and moisture uptake, as well as disrupt parasitic and pathogenic microbes in the host plant. Such benefits may enable invaders to establish significant and persistent populations in environments previously dominated by natives. Coupling these findings with studies on invader pathogen-disrupting root exudates is not well documented in the literature describing plant invasion strategies. The interaction effects of altered AMF associations and the impact of invader root exudates would be more relevant than understanding the AMF dynamics or the phytochemistry of successful invaders in isolation, particularly given that AMF and root exudates can have a similar role in pathogen control but function quite differently. One means to achieve this goal is to assess these strategies concurrently by characterizing both the general (mostly pathogens or commensals) and AM-specific fungal colonization patterns found in field collected root samples of successful invaders, native plants growing within dense patches of invaders, and native plants growing separately from invaders. In this review I examine the emerging evidence of the ways in which AMF-plant interactions and the production of defensive root exudates provide pathways to invasive plant establishment and expansion, and conclude that interaction studies must be pursued to achieve a more comprehensive understanding of successful plant 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".