Evaluation of the soil microbiome of three raised beaches in the Devon Island Lowland, High Arctic, Canada
Bibliographic record
Abstract
The Arctic region is characterized by severe temperatures and a unique ecosystem with largely unexplored microbiomes. Whereas soil microbiomes in temperate regions play key roles in nutrient cycling, organic matter decomposition, greenhouse gas fluxes, and overall ecosystem functioning, Arctic microbiomes remain poorly understood, highlighting the need for a thorough characterization to better predict and manage soil health and resilience. In this study, we compared the microbial profiles of three raised beaches on Devon Island (Nunavut, Canadian Arctic Archipelago), which emerged sequentially between eight and two thousand years ago, to assess their similarities and differences. Samples were collected by genetic horizons along excavations from the top layer to the permafrost. For each horizon, total soil DNA, 16S gene copies dPCR quantification, 16S metabarcoding, and functional prediction were carried out. Total DNA quantification revealed a consistently comparable concentration of genetic material across the three soil beaches (AB2 μ = 2.28 ± 5.44 μg ∙ g-1, AB1 μ = 4.71 ± 2.35 μg ∙ g-1, AB3 μ = 5.44 ± 2.91 μg ∙ g-1), regardless of site age (AB2 = 2,360 YBP, AB1 = 6,726 YBP, AB3 = 8,410 YBP). Conversely, clear differences emerged by comparing the different horizons at each site. The hierarchical cluster analysis based on the Bray-Curtis dissimilarity matrix revealed a clear separation between surface and deep horizons. The core microbiome analysis highlighted Actinobacteria, Proteobacteria, and Firmicutes as the three predominant phyla accounting for relative abundances of 42%, 22%, and 18%, respectively. Remarkable evidence was the unexpectedly high taxonomic diversity that was recorded in these sites and that surprisingly matched with the commonly observed values in soils of temperate regions. Since these stony shores developed under cold, life-limiting conditions, their apparent microbial richness raises doubts about the potential biases in inferring physiological contexts and active biodiversity directly inferred from culture-independent DNA-based studies. The reason is that such inventories can be possibly inflated, in all environments, by chronically accumulated cells from passive immigration events through atmospheric discharge.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".