High Arctic seawater and coastal soil microbiome co-occurrence and composition structure and their potential hydrocarbon biodegradation
Bibliographic record
Abstract
Abstract The accelerated decline in Arctic sea-ice cover and duration is enabling the opening of Arctic marine passages and improving access to natural resources. The increasing accessibility to navigation and resource exploration and production brings risks of accidental hydrocarbon releases into Arctic waters, posing a major threat to Arctic marine ecosystems where oil may persist for many years, especially in beach sediment. The composition and response of the microbial community to oil contamination on Arctic beaches remain poorly understood. To address this, we analyzed microbial community structure and identified hydrocarbon degradation genes among the Northwest Passage intertidal beach sediments and shoreline seawater from five high Arctic beaches. Our results from 16S/18S rRNA genes, long-read metagenomes, and metagenome-assembled genomes reveal the composition and metabolic capabilities of the hydrocarbon microbial degrader community, as well as tight cross-habitat and cross-kingdom interactions dominated by lineages that are common and often dominant in the polar coastal habitat, but distinct from petroleum hydrocarbon-contaminated sites. In the polar beach sediment habitats, Granulosicoccus sp. and Cyclocasticus sp. were major potential hydrocarbon-degraders, and our metagenomes revealed a small proportion of microalgae and algal viruses possessing key hydrocarbon biodegradative genes. This research demonstrates that Arctic beach sediment and marine microbial communities possess the ability for hydrocarbon natural attenuation. The findings provide new insights into the viral and microalgal communities possessing hydrocarbon degradation genes and might represent an important contribution to the removal of hydrocarbons under harsh environmental conditions in a pristine, cold, and oil-free environment that is threatened by oil spills.
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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 | 0.001 | 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) | 0.001 | 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".