Host plant and soil nutrient filters mediate long‐term grazing on arbuscular mycorrhizal fungi in desert grasslands
Bibliographic record
Abstract
Arbuscular mycorrhizal fungi (AMF) enhance plant performance through improved nutrient acquisition, stress resilience, and pathogen resistance while strengthening ecosystem functions through soil structure stabilization and carbon sequestration. Livestock grazing is the dominant grassland land-use globally, but the effects of increased grazing intensity on AMF remain debated. Importantly, the mechanistic drivers of AMF responses to grazing intensity remain poorly understood, particularly in arid grasslands. Based on an 18-yr experiment with four grazing intensities (no grazing, light grazing, moderate grazing, and heavy grazing) in a desert grassland in Inner Mongolia, we examined the response of the AMF community to grazing and the mechanisms underlying the observed changes in AMF communities. AMF diversity, as well as the number of nodes, edges, and overall complexity of the AMF inter-species network, decreased progressively from no grazing to heavy grazing. Grazing also altered AMF community composition, with a significant increase in the abundance of the genus Glomus under heavy grazing. These changes in AMF communities were dominated by deterministic processes. Specifically, intensifying grazing is accompanied by reduced plant diversity and soil nutrient availability, as well as the prevalence of more stress-tolerant plant ecological strategies, all of which contribute to the simplification of AMF communities. Our results demonstrate that both host plants and soil nutrient availability are the key drivers shaping AMF communities in grazed desert grasslands. Given the important functions of AMF and the negative impacts of long-term grazing on it, there is an urgency to promote diverse grazing systems and reduce grazing pressure to improve grassland management.
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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 | 0.001 | 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) | 0.000 | 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".