Mycorrhizal fungi reduce fitness differences, but coexistence is determined by differences in intrinsic plant mycorrhizal responsiveness
Bibliographic record
Abstract
Abstract Plant–arbuscular mycorrhizal fungal (AMF) associations can mediate soil resources among competing plants to influence plant resource capture and fitness, making AMF a potential agent of plant coexistence. We assessed plant coexistence, via niche and fitness differences, using six plant species varying in their mycorrhizal status. We grew the species in 15 competitive pairs with or without AMF. Effects of AMF on coexistence were determined by parametrizing pair-wise Lotka–Volterra plant competition models. Responses of the six plant species to AMF were determined by comparing the shoot biomass of single plants grown in the absence of any competition with AMF to the shoot biomass without AMF. The inoculation with AMF reduced the fitness differences between competitors, but the degree of AMF-mediated coexistence depended on the identity of the competing plant species. A greater AMF response difference between competing plant species reduced niche overlap and increased coexistence. These results show that while AMF generally reduce fitness differences, the equalizing effect of AMF is not always strong enough to overcome a competitive imbalance due to niche overlap and thus does not always lead to coexistence. Instead, it is the intrinsic growth response of different plant species to AMF, which can predict reduced niche overlap that in turn leads to coexistence. This implies that mycorrhizal dependence is a plant strategy to reduce niche overlap with competitors, thus allowing for greater coexistence.
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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".