Impacts of large herbivores on mycorrhizal fungal communities across the Arctic
Bibliographic record
Abstract
Mycorrhizal fungi play an integral role in nutrient and carbon cycling in soils, which may be especially important in the Arctic, one of the world's most soil carbon‐rich regions. Large mammalian herbivores can influence these fungi through their impacts on vegetation and soil conditions, however the strength and prevalence of these interactions in the Arctic is still uncertain. We collected soils from 15 large mammal exclusion experiments across the Arctic. We sequenced both ITS regions and partial SSU regions using two sets of amplicons to determine the composition of soil mycorrhizal fungal communities. This allowed us to assess how these communities are impacted by exclusion of large mammalian herbivores, plant communities, and climate and soil properties. Large mammalian herbivore exclusion had a significant impact on the arbuscular mycorrhizal (AM) community dissimilarity between sites. The AM community was also influenced by growing season temperature and pH, which may indicate that conditions are becoming more favourable for these species in some Arctic communities. Large herbivore exclusion did not have a coherent impact on ectomycorrhizal and ericoid mycorrhizal fungal community dissimilarity, which were primarily correlated with δ 15 N signature in the soil, rather than herbivory, climate, or plant functional types. The consistent detection of arbuscular mycorrhizal fungi identified highlights the need for more thorough evaluations of these communities and their role in Arctic carbon and nutrient dynamics, as these fungi are currently understudied in the Arctic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".