Vegetation-Associated Impacts on Arctic Tundra Bacterial and Microeukaryotic Communities
Bibliographic record
Abstract
The Arctic is experiencing rapid vegetation changes, such as shrub and tree line expansion, due to climate warming, as well as increased wetland variability due to hydrological changes associated with permafrost thawing. These changes are of global concern because changes in vegetation may increase tundra soil biogeochemical processes that would significantly enhance atmospheric CO2 concentrations. Predicting the latter will at least partly depend on knowing the structure, functional activities, and distributions of soil microbes among the vegetation types across Arctic landscapes. Here we investigated the bacterial and microeukaryotic community structures in soils from the four principal low Arctic tundra vegetation types: wet sedge, birch hummock, tall birch, and dry heath. Sequencing of rRNA gene fragments indicated that the wet sedge and tall birch communities differed significantly from each other and from those associated with the other two dominant vegetation types. Distinct microbial communities were associated with soil pH, ammonium concentration, carbon/nitrogen (C/N) ratio, and moisture content. In soils with similar moisture contents and pHs (excluding wet sedge), bacterial, fungal, and total eukaryotic communities were correlated with the ammonium concentration, dissolved organic nitrogen (DON) content, and C/N ratio. Operational taxonomic unit (OTU) richness, Faith's phylogenetic diversity, and the Shannon species-level index (H') were generally lower in the tall birch soil than in soil from the other vegetation types, with pH being strongly correlated with bacterial richness and Faith's phylogenetic diversity. Together, these results suggest that Arctic soil feedback responses to climate change will be vegetation specific not just because of distinctive substrates and environmental characteristics but also, potentially, because of inherent differences in microbial community structure.
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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) | 0.001 | 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 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".