The emerging science of linked plant–fungal invasions
Bibliographic record
Abstract
Summary Invasions of alien plants are typically studied as invasions of individual species, yet interactions between plants and symbiotic fungi (mutualists and potential pathogens) affect plant survival, physiological traits, and reproduction and hence invasion success. Studies show that plant–fungal associations are frequently key drivers of plant invasion success and impact, but clear conceptual frameworks and integration across studies are needed to move beyond a series of case studies towards a more predictive understanding. Here, we consider linked plant–fungal invasions from the perspective of plant and fungal origin, simplified to the least complex representations or ‘motifs’. By characterizing these interaction motifs, parallels in invasion processes between pathogen and mutualist fungi become clear, although the outcomes are often opposite in effect. These interaction motifs provide hypotheses for fungal‐driven dynamics behind observed plant invasion trajectories. In some situations, the effects of plant–fungal interactions are inconsistent or negligible. Variability in when and where different interaction motifs matter may be driven by specificity in the plant–fungal interaction, the size of the effect of the symbiosis (negative to positive) on plants and the dependence (obligate to facultative) of the plant−fungal interaction. Linked plant–fungal invasions can transform communities and ecosystem function, with potential for persistent legacies preventing ecosystem restoration. Contents Summary 1314 I. Introduction 1315 II. Simplification of complex networks into interaction motifs 1316 III. Plant–fungal interactions and plant invasion dynamics 1322 IV. Predicting where plant–fungal interactions matter: plant−symbiont specificity, dependence and effect size 1323 V. Spatial context of linked plant–fungal invasions 1325 VI. Impacts of linked plant–fungal invasions on ecosystem processes 1326 VII. Management implications 1326 VIII. Conclusions 1327 Acknowledgements 1328 References 1328
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".