Shotgun metagenomics reveals the flexibility and diversity of Arctic marine microbiomes
Bibliographic record
Abstract
Abstract Polar oceanographic regions are exposed to rapid changes in temperature, salinity, and light fields that determine microbial species distributions, but resilience to an increasingly unstable climate is unknown. To unravel microbial genomic potential of the Northern Baffin Bay’s polynya, we constructed eight metagenomes from the same latitude but targeting two sides of Pikialasorsuaq (The North Water) that differ by current systems, stratification, and temperature regimes. Samples from the surface and subsurface chlorophyll maximum (SCM) of both sides were collected 13 months apart. Details of metabolic pathways were determined for 18 bacteria and 10 microbial eukaryote metagenome-assembled genomes (MAGs). The microbial eukaryotic MAGs were associated with the dominant green algae in the Mamiellales and diatoms in the Mediophyceae, which tended to respectively dominate the eastern and western sides of Pikialasorsuaq. We show that microbial community taxonomic and functional signatures were ca. 80% similar at the latitude sampled with only 20% of genes associated with local conditions. From the metagenomes we found genes involved in osmotic regulation, antifreeze proteins, and photosystem protection, with hydrocarbon biodegradation and methane oxidation potential detected. The shared genomic compliment was consistent with adaptation to the Arctic’s extreme fluctuating conditions, with implications for their evolutionary history and the long-term survival of a pan-arctic microbiome. In particular, previously unrecognized genetic capabilities for methane bio-attenuation and hydrocarbon metabolism in eukaryotic phytoplankton suggest adaptation to dark conditions that will remain, despite climate warming, in the high latitude offshore waters of a future Arctic.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".