Glacial Runoff Promotes Deep Burial of Sulfur Cycling-Associated Microorganisms in Marine Sediments
Bibliographic record
Abstract
Marine fjords with active glacier outlets are hot spots for organic matter burial in the sediments and subsequent microbial mineralization. Here, we investigated controls on microbial community assembly in sub-arctic glacier-influenced (GI) and non-glacier-influenced (NGI) marine sediments in the Godthåbsfjord region, south-western Greenland. We used a correlative approach integrating 16S rRNA gene and dissimilatory sulfite reductase (dsrB) amplicon sequence data over six meters of depth with biogeochemistry, sulfur-cycling activities, and sediment ages. GI sediments were characterized by comparably high sedimentation rates and had ‘young’ sediment ages of <500 years even at 6 m sediment depth. In contrast, NGI stations reached ages of approximately 10,000 years at these depths. Sediment age-depth relationships, sulfate reduction rates, and C/N ratios were strongly correlated with differences in microbial community composition between GI and NGI sediments, indicating that age and diagenetic state were key drivers of microbial community assembly in subsurface sediments. Similar bacterial and archaeal communities were present in the surface sediments of all stations, whereas only in GI sediments were many surface taxa also abundant through the whole sediment core. The relative abundance of these taxa, including diverse Desulfobacteraceae members, correlated positively with sulfate reduction rates, indicating their active contributions to sulfur-cycling processes. In contrast, other surface community members, such as Desulfatiglans, Atribacteria and Chloroflexi, survived the slow sediment burial at NGI stations and dominated in the deepest sediment layers. These taxa are typical for the energy-limited marine deep biosphere and their relative abundances correlated positively with sediment age. In conclusion, our data suggests that high rates of sediment accumulation caused by glacier runoff and associated changes in biogeochemistry, promote persistence of sulfur-cycling activity and burial of a larger fraction of the surface microbial community into the deep subsurface.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".