The influence of arbuscular mycorrhizae on the relationship between plant diversity and productivity
Bibliographic record
Abstract
Ecological theory predicts a positive and asymptotic relationship between plant diversity and ecosystem productivity based on the ability of more diverse plant communities to use limiting resources more fully. This is supported by recent empirical evidence. Additionally, in natural ecosystems, plant productivity is often a function of the presence and composition of mycorrhizal associations. Yet, the effect of mycorrhizal fungi on the relationship between plant diversity and productivity has not been investigated. We predict that in the presence of AMF, productivity will saturate at lower levels of species richness because AMF increase the ability of plant species to utilize nutrient resources. In this study we manipulated old‐field plant species richness in the presence and absence of two species of AMF. We found that in the absence of AMF, the relationship between plant species richness and productivity is positive and linear. However, in the presence of AMF, the relationship is positive but asymptotic, even though the maximum plant biomass was significantly different between the two AMF treatments. This is consistent with the hypothesis that AMF increase the redundancy of plant species in the productivity of plant communities, and indicates that these symbionts must be considered in future investigations of plant biodiversity and ecosystem function.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".