Arbuscular mycorrhizas influence plant diversity, productivity, and nutrients in boreal grasslands
Bibliographic record
Abstract
The effect of reduced arbuscular mycorrhiza (AM) colonization on nitrogen and phosphorus concentration in grass tissue, species diversity, cover, and productivity was investigated after 4 years of benomyl application in two boreal grasslands, Storvordlia (a high plant diversity field) and Kalvsvangen (a low plant diversity field), in Eastern Norway. In addition, AM colonization, spore numbers, and mycorrhizal inoculum potential was studied during one season. A split-plot design was used, with grazing as the main treatment and the application of benomyl as a subtreatment. AM colonization was significantly reduced because of benomyl application, whereas spore numbers were not significantly affected by the treatment. Mycorrhizal inoculum potential was reduced, but not consistently. In both sites nitrogen and phosphorus concentration of grass tissue was significantly higher in grazed plots than in ungrazed ones, and significantly lower in benomyl subplots than in controls. Plant diversity, composition, and cover had changed significantly after 4 years in grazed controls in Storvordlia, but this was not seen in Kalvsvangen. In both sites, total productivity increased significantly after 4 years of benomyl application. This experiment indicates that AM associations can influence grass nutrient contents and species responses, plant diversity, and productivity in boreal grasslands. These differences were larger in the higher plant diversity site, which is situated at a higher elevation and is under more marginal growth conditions.Key words: arbuscular mycorrhiza, benomyl, productivity, biodiversity, boreal grasslands, ecosystem processes.
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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".