Diverse plant mixtures sustain a greater arbuscular mycorrhizal fungi spore viability than monocultures after 12 years
Bibliographic record
Abstract
Abstract Aims Intensive land management practices can compromise soil biodiversity, thus jeopardizing long-term soil productivity. Arbuscular mycorrhizal fungi (AMF) play a pivotal role in promoting soil productivity through obligate symbiotic associations with plants. However, it is not clear how properties of plant communities, especially species richness and composition influence the viability of AMF populations in soils. Methods Here we test whether monocultures of eight plant species from different plant functional groups, or a diverse mixture of plant species, maintain more viable AMF propagules. To address this question, we extracted AMF spores from 12-year old plant monocultures and mixtures and paired single AMF spores with single plants in a factorial design crossing AMF spore origin with plant species identity. Important Findings AMF spores from diverse plant mixtures were more successful at colonizing multiple plant species and plant individuals than AMF spores from plant monocultures. Furthermore, we found evidence that AMF spores originating from diverse mixtures more strongly increased biomass than AMF from monocultures in the legume Trifolium repens L. AMF viability and ability to interact with many plant species were greater when AMF spores originated from 12-year old mixtures than monocultures. Our results show for the first time that diverse plant communities can sustain AMF viability in soils and demonstrate the potential of diverse plant communities to maintain viable AMF propagules that are a key component to soil health and productivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".