Long-term experimental manipulation of climate alters the ectomycorrhizal community of Betula nana in Arctic tundra
Bibliographic record
Abstract
Climate warming is leading to shrub expansion in Arctic tundra. Shrubs form ectomycorrhizal (ECM) associations with soil fungi that are central to ecosystem carbon balance as determinants of plant community structure and as decomposers of soil organic matter. To assess potential climate change impacts on ECM communities, we analysed fungal internal transcribed spacer sequences from ECM root tips of the dominant tundra shrub Betula nana growing in treatments plots that had received long-term warming by greenhouses and/or fertilization as part of the Arctic Long-Term Ecological Research experiment at Toolik Lake Alaska, USA. We demonstrate opposing effects of long-term warming and fertilization treatments on ECM fungal diversity; with warming increasing and fertilization reducing the diversity of ECM communities. We show that warming leads to a significant increase in high biomass fungi with proteolytic capacity, especially Cortinarius spp., and a reduction of fungi with high affinities for labile N, especially Russula spp. In contrast, fertilization treatments led to relatively small changes in the composition of the ECM community, but increased the abundance of saprotrophs. Our data suggest that warming profoundly alters nutrient cycling in tundra, and may facilitate the expansion of B. nana through the formation of mycorrhizal networks of larger size.
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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 | 0.001 |
| 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".