Plant geographic origin and phylogeny as potential drivers of community structure in root‐inhabiting fungi
Bibliographic record
Abstract
Abstract Root‐inhabiting fungal communities, including mutualists and antagonists, influence host plant performance, and can potentially shape plant community composition. However, there is uncertainty about how root‐inhabiting fungal communities are structured, and if fungal community characteristics are significant predictors of host plant abundance. In this study, we first assessed how root‐inhabiting fungal communities were structured in relation to the phylogeny and geographic origins (native vs. exotic) of their host plants in an old‐field community. In addition, we took into consideration the spatial arrangements (i.e. physical locations) of the individual host plants. We then tested if the relative abundances of pathogenic and beneficial arbuscular mycorrhizal (AM) fungi could predict host plant abundances. We found that host plant phylogeny was an important factor in structuring the whole fungal community, irrespective of host plant origin. Furthermore, the spatial arrangements of individual host plants were a strong predictor of AM fungal community structure. Host plant phylogeny and spatial arrangements appeared to similarly affect the structure of pathogenic fungal communities. No distinct differences were observed between native and exotic plant species in fungal community characteristics. The relative abundances of AM and pathogenic fungi were not significant predictors for observed abundances of their host plants. Synthesis. Host plant phylogeny and spatial arrangements can structure naturally occurring root‐inhabiting fungal communities. The absence of distinct differences in fungal community composition, including pathogens, in exotic and native plants suggests long residence times and the consequent naturalization of exotic species in the region, allowing for the establishment of similar plant–microbial interactions between native and exotic species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".