Re‐examining the evidence for the mother tree hypothesis – resource sharing among trees via ectomycorrhizal networks
Bibliographic record
Abstract
Seminal scientific papers positing that mycorrhizal fungal networks can distribute carbon (C) among plants have stimulated a popular narrative that overstory trees, or 'mother trees', support the growth of seedlings in this way. This narrative has far-reaching implications for our understanding of forest ecology and has been controversial in the scientific community. We review the current understanding of ectomycorrhizal C metabolism and observations on forest regeneration that make the mother tree narrative debatable. We then re-examine data and conclusions from publications that underlie the mother tree hypothesis. Isotopic labeling methods are uniquely suited for studying element fluxes through ecosystems, but the complexity of mycorrhizal symbiosis, low detection limits, and small carbon discrimination in biological processes can cause researchers to make important inferences based on miniscule shifts in isotopic abundance, which can be misleading. We conclude that evidence of a significant net C transfer via common mycorrhizal networks that benefits the recipients is still lacking. Furthermore, a role for fungi as a C pipeline between trees is difficult to reconcile with any adaptive advantages for the fungi. Finally, the hypothesis is neither supported by boreal forest regeneration patterns nor consistent with the understanding of physiological mechanisms controlling mycorrhizal symbiosis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".