Plant assemblage composition and soil P concentration differentially affect communities of AM and total fungi in a semi-arid grassland
Bibliographic record
Abstract
Adding inorganic P- and N-fixing legumes to semi-arid grasslands can increase forage yield, but soil nutrient concentrations and plant cover may also interact to modify soil fungal populations, impacting short- and long-term forage production. We tested the effect of plant assemblage (seven native grasses, seven native grasses + the domesticated N-fixing legume Medicago sativa, seven native grasses + the native N-fixing legume Dalea purpurea or the introduced grass Bromus biebersteinii + M. sativa) and soil P concentration (addition of 0 or 200 P2O5 kg ha(-1) at sowing) on the diversity and community structure of arbuscular mycorrhizal (AM) fungi and total fungi over two consecutive years, using 454-pyrosequencing of 18S rDNA and ITS amplicons. Treatment effects were stronger in the wet year (2008) than the dry year (2009). The presence of an N-fixing legume with native grasses generally increased AM fungal diversity, while the interaction between soil P concentration and plant assemblage modified total fungal community structure in 2008. Excluding interannual variations, which are likely driven by moisture and plant productivity, AM fungal communities in semi-arid grasslands appear to be primarily affected by plant assemblage composition, while the composition of other fungi is more closely linked to soil P.
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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".