Dual‐mycorrhizal plants: their ecology and relevance
Bibliographic record
Abstract
Dual-mycorrhizal plants are capable of associating with fungi that form characteristic arbuscular mycorrhizal (AM) and ectomycorrhizal (EM) structures. Here, we address the following questions: (1) How many dual-mycorrhizal plant species are there? (2) What are the advantages for a plant to host two, rather than one, mycorrhizal types? (3) Which factors can provoke shifts in mycorrhizal dominance (i.e. mycorrhizal switching)? We identify a large number (89 genera within 32 families) of confirmed dual-mycorrhizal plants based on observing arbuscules or coils for AM status and Hartig net or similar structures for EM status within the same plant species. We then review the possible nutritional benefits and discuss the possible mechanisms leading to net costs and benefits. Cost and benefits of dual-mycorrhizal status appear to be context dependent, particularly with respect to the life stage of the host plant. Mycorrhizal switching occurs under a wide range of abiotic and biotic factors, including soil moisture and nutrient status. The relevance of dual-mycorrhizal plants in the ecological restoration of adverse sites where plants are not carbon limited is discussed. We conclude that dual-mycorrhizal plants are underutilized in ecophysiological-based experiments, yet are powerful model plant-fungal systems to better understand mycorrhizal symbioses without confounding host effects.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".