Mycorrhizal networks: a review of their extent, function, and importance
Bibliographic record
Abstract
It is well known from laboratory studies that a single mycorrhizal fungal isolate can colonize different plant species, form interplant linkages, and provide a conduit for interplant transfer of isotopic carbon, nitrogen, phosphorus, or water. There is increasing laboratory and field evidence that the magnitude and direction of transfer is influenced by physiological sourcesink gradients between plants. There is also evidence that mycorrhizal fungi play a role in regulating transfer through their own sourcesink patterns, frequency of links, and mycorrhizal dependency. Although it is plausible that connections are extensive in nature, field studies have been hampered by our inability to observe them in situ and by belowground complexity. In future, isotopic tracers, morphological observations, microsatellite techniques, and fluorescent dyes will be useful in the study of networks in nature. Mycorrhizal networks have the potential to influence patterns of seedling establishment, interplant competition, plant diversity, and plant community dynamics, but studies in this area are just beginning. Future plant community studies would benefit from concurrent experimental use of fungal network controls, isotopic labeling, direct observation of interplant linkages, and long-term observation in the field. In this paper, we review recent literature on mycorrhizal networks and interplant carbon transfer, suggest future research directions, and highlight promising scientific approaches.Key words: common mycorrhizal network, carbon transfer, sourcesink, establishment, competition, diversity.
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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 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".