Access to mycorrhizal networks and roots of trees: importance for seedling survival and resource transfer
Bibliographic record
Abstract
Mycorrhizal networks (MNs) are fungal hyphae that connect roots of at least two plants. It has been suggested that these networks are ecologically relevant because they may facilitate interplant resource transfer and improve regeneration dynamics. This study investigated the effects of MNs on seedling survival, growth and physiological responses, interplant resource (carbon and nitrogen) transfer, and ectomycorrhizal (EM) fungal colonization of seedlings by trees in dry interior Douglas-fir (Pseudotsuga menziesii var. glauca) forests. On a large, recently harvested site that retained some older trees, we established 160 isolated plots containing pairs of older Douglas-fir "donor" trees and either manually sown seed or planted Douglas-fir "receiver" seedlings. Seed- and greenhouse-grown seedlings were sown and planted into four mesh treatments that served to restrict MN access (i.e., planted into mesh bags with 0.5-, 35-, 250-microm pores, or without mesh). Older trees were pulse labeled with carbon (13CO2) and nitrogen (15NH4(15)NO3) to quantify resource transfer. After two years, seedlings grown from seed in the field had the greatest survival and received the greatest amounts of transferred carbon (0.0063% of donor photo-assimilates) and nitrogen (0.0018%) where they were grown without mesh; however, planted seedlings were not affected by access to tree roots and hyphae. Size of "donor" trees was inversely related to the amount of carbon transferred to seedlings. The potential for MNs to form was high (based on high similarity of EM communities between hosts), and MN-mediated colonization appeared only to be important for seedlings grown from seed in the field. These results demonstrate that MNs and mycorrhizal roots of trees may be ecologically important for natural regeneration in dry forests, but it is still uncertain whether resource transfer is an important mechanism underlying seedling establishment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".