Arbuscular mycorrhizal fungi in changing mountain grassland ecosystems: a challenge for research
Bibliographic record
Abstract
Even though arbuscular mycorrhizal fungi (AMF) are present from foothills to all alpine habitats, research on their role in mountain ecosystems remains incomplete. Here we provide a literature review of the ecology and functioning of AMF in mountain ecosystems, as well as their response to global change. We investigated how AM fungal abundance, community composition, and fungal traits are studied under field conditions and are affected by altitude, habitat patchiness, succession, host identity, seasonality, and interaction with other living organisms. The effects of climate change, nutrient enrichment, land-use change, and their interactions are also addressed in this review, as well as the possible applications of AMF for grassland restoration. We show that altitudinal effects on AMF are blurred by other environmental gradients and by host-plant identity. The benefits to plants and possible facilitation effects by AMF in mountain ecosystems have not yet been identified. Based on the stress-gradient hypothesis, the symbiosis between plants and AMF should become more mutualistic with increasing environmental stress. We propose a working hypothesis for the functioning of the AMF–plant interaction along altitudinal gradients by grading it along the mutualism–parasitism continuum, considering current and global climate and environmental change conditions. We conclude by suggesting further research directions.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".