Species richness of arbuscular mycorrhizal fungi: associations with grassland plant richness and biomass
Bibliographic record
Abstract
Although experiments show a positive association between vascular plant and arbuscular mycorrhizal fungal (AMF) species richness, evidence from natural ecosystems is scarce. Furthermore, there is little knowledge about how AMF richness varies with belowground plant richness and biomass. We examined relationships among AMF richness, above- and belowground plant richness, and plant root and shoot biomass in a native North American grassland. Root-colonizing AMF richness and belowground plant richness were detected from the same bulk root samples by 454-sequencing of the AMF SSU rRNA and plant trnL genes. In total we detected 63 AMF taxa. Plant richness was 1.5 times greater belowground than aboveground. AMF richness was significantly positively correlated with plant species richness, and more strongly with below- than aboveground plant richness. Belowground plant richness was positively correlated with belowground plant biomass and total plant biomass, whereas aboveground plant richness was positively correlated only with belowground plant biomass. By contrast, AMF richness was negatively correlated with belowground and total plant biomass. Our results indicate that AMF richness and plant belowground richness are more strongly related with each other and with plant community biomass than with the plant aboveground richness measures that have been almost exclusively considered to date.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".