Pole-to-pole biogeography of surface and deep marine bacterial communities
Bibliographic record
Abstract
The Antarctic and Arctic regions offer a unique opportunity to test factors shaping biogeography of marine microbial communities because these regions are geographically far apart, yet share similar selection pressures. Here, we report a comprehensive comparison of bacterioplankton diversity between polar oceans, using standardized methods for pyrosequencing the V6 region of the small subunit ribosomal (SSU) rRNA gene. Bacterial communities from lower latitude oceans were included, providing a global perspective. A clear difference between Southern and Arctic Ocean surface communities was evident, with 78% of operational taxonomic units (OTUs) unique to the Southern Ocean and 70% unique to the Arctic Ocean. Although polar ocean bacterial communities were more similar to each other than to lower latitude pelagic communities, analyses of depths, seasons, and coastal vs. open waters, the Southern and Arctic Ocean bacterioplankton communities consistently clustered separately from each other. Coastal surface Southern and Arctic Ocean communities were more dissimilar from their respective open ocean communities. In contrast, deep ocean communities differed less between poles and lower latitude deep waters and displayed different diversity patterns compared with the surface. In addition, estimated diversity (Chao1) for surface and deep communities did not correlate significantly with latitude or temperature. Our results suggest differences in environmental conditions at the poles and different selection mechanisms controlling surface and deep ocean community structure and diversity. Surface bacterioplankton may be subjected to more short-term, variable conditions, whereas deep communities appear to be structured by longer water-mass residence time and connectivity through ocean circulation.
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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 | 0.000 |
| 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".