Mycorrhizal fungi supply nitrogen to host plants in Arctic tundra and boreal forests: 15N is the key signal
Bibliographic record
Abstract
Symbiotic fungi’s role in providing nitrogen to host plants is well-studied in tundra at Toolik Lake, Alaska, but little-studied in the adjoining boreal forest ecosystem. Along a 570 km north–south transect from the Yukon River to the North Slope of Alaska, the 15N content was strongly reduced in ectomycorrhizal and ericoid mycorrhizal plants including Betula, Salix, Picea mariana (P. Mill.) B.S.P., Picea glauca Moench (Voss), and ericaceous plants. Compared with the 15N content of soil, the foliage of nonmycorrhizal plants (Carex and Eriophorum) was unchanged, whereas content of the ectomycorrhizal fungi was very much higher (e.g., Boletaceae, Leccinum and Cortinarius). It is hypothesized that similar processes operate in tundra and boreal forest, both nitrogen-limited ecosystems: (i) mycorrhizal fungi break down soil polymers and take up amino acids or other nitrogen compounds; (ii) mycorrhizal fungi fractionate against 15N during production of transfer compounds; (iii) host plants are accordingly depleted in 15N; and (iv) mycorrhizal fungi are enriched in 15N. Increased N availability for plant roots or decreased light availability to understory plants may have decreased N allocation to mycorrhizal partners and increased δ15N by 3‰–4‰ for southern populations of Vaccinium vitis-idaea L. and Salix. Fungal biomass, measured as ergosterol, correlated strongly with soil organic matter and attained amounts similar to those in temperate forest soils.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".