Mycorrhizal networks counteract competitive effects of canopy trees on seedling survival
Bibliographic record
Abstract
The dynamics of forest ecosystems depend largely on the survival of seedlings in their understories, but seedling survival is known to be limited by preemption of light and soil resources by overstory trees. It has been hypothesized that "common mycorrhizal networks," wherein roots of seedlings are linked to overstory trees by symbiotic mycorrhizal fungi, offset some or all of the negative effects of trees on seedlings. Here we report the results of an unambiguous experimental test of this hypothesis in a monodominant Pinus radiata forest. We also tested the hypothesis that adaptive differentiation among plant populations causes local plant genotypes to respond more positively to mycorrhizal networks than nonlocal plant genotypes. Our results demonstrate large positive effects of overstory mycorrhizal networks on seedling survival, along with simultaneous negative effects of tree roots, regardless of whether plant genotypes were locally derived. Physiological and leaf-chemistry measurements suggest that seedlings connected to common mycorrhizal networks benefited from increased access to soil water. The similar magnitude of the positive and negative overstory effects on seedlings and the ubiquity of mycorrhizal networks in forests suggest that mycorrhizal networks fundamentally influence the demographic and community dynamics of forest trees.
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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 | 0.001 |
| 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 | 0.001 |
| 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".