Hydrocarbon seepage in the deep seabed links subsurface and seafloor biospheres
Bibliographic record
Abstract
Marine cold seeps transmit fluids between the subseafloor and seafloor biospheres through upward migration of hydrocarbons that originate in deep sediment layers. It remains unclear how geofluids influence the composition of the seabed microbiome and if they transport deep subsurface life up to the surface. Here we analyzed 172 marine surficial sediments from the deep-water Eastern Gulf of Mexico to assess whether hydrocarbon fluid migration is a mechanism for upward microbial dispersal. While 132 of these sediments contained migrated liquid hydrocarbons, evidence of continuous advective transport of thermogenic alkane gases was observed in 11 sediments. Gas seeps harbored distinct microbial communities featuring bacteria and archaea that are well-known inhabitants of deep biosphere sediments. Specifically, 25 distinct sequence variants within the uncultivated bacterial phyla Atribacteria and Aminicenantes and the archaeal order Thermoprofundales occurred in significantly greater relative sequence abundance along with well-known seep-colonizing members of the bacterial genus Sulfurovum , in the gas-positive sediments. Metabolic predictions guided by metagenome-assembled genomes suggested these organisms are anaerobic heterotrophs capable of nonrespiratory breakdown of organic matter, likely enabling them to inhabit energy-limited deep subseafloor ecosystems. These results point to petroleum geofluids as a vector for the advection-assisted upward dispersal of deep biosphere microbes from subsurface to surface environments, shaping the microbiome of cold seep sediments and providing a general mechanism for the maintenance of microbial diversity in the deep sea.
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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) | 0.001 | 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".