Beyond seedlings: ectomycorrhizal fungal networks and growth of mature Pseudotsuga menziesii
Bibliographic record
Abstract
1. Mycorrhizal networks are conduits for the transfer of resources between hosts. While ectomycorrhizal networks (EMN) are known to influence seedlings, their effect on adult tree growth remains unknown and may have important implications for forest responses to future climates. 2. We used annual basal area increment of trees and previously described Rhizopogon vesiculosus and Rhizopogon vinicolor EMNs to examine an association between the number of connections between trees through an EMN and the growth of adult interior Douglas-fir. We compared this relationship for the year the networks were mapped, in 2008, with eight years previous and eight years afterward. We also compared the variation in standardized growth (2000–2016) to examine the association between growth variability and EMN variables. 3. Greater growth was positively associated with 1) the number of connections to other trees via a Rhizopogon vinicolor EMN, and 2) the number of genets of Rhizopogon vesiculosis by which a tree was colonized. Variation of growth (2000–2016) was negatively associated with increasing number of connections to other trees via Rhizopogon vinicolor. Synthesis. These findings, for the first time, indicate that EMNs may positively influence the growth of adult trees. The difference in tree growth response between the sister fungal species highlights a novel avenue to identify interspecific and intraspecific differences between species occurring at different depths in the soil. Our study has important implications when considering the role of EMNs in influencing forest health and mitigating stress from environmental conditions.
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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) | 0.000 | 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".