Hydrography shapes bacterial biogeography of the deep Arctic Ocean
Bibliographic record
Abstract
It has been long debated as to whether marine microorganisms have a ubiquitous distribution or patterns of biogeography, but recently a consensus for the existence of microbial biogeography is emerging. However, the factors controlling the distribution of marine bacteria remain poorly understood. In this study, we combine pyrosequencing and traditional Sanger sequencing of the 16S rRNA gene to describe in detail bacterial communities from the deep Arctic Ocean. We targeted three separate water masses, from three oceanic basins and show that bacteria in the Arctic Ocean have a biogeography. The biogeographical distribution of bacteria was explained by the hydrography of the Arctic Ocean and subsequent circulation of its water masses. Overall, this first taxonomic description of deep Arctic bacteria communities revealed an abundant presence of SAR11 (Alphaproteobacteria), SAR406, SAR202 (Chloroflexi) and SAR324 (Deltaproteobacteria) clusters. Within each cluster, the abundance of specific phylotypes significantly varied among water masses. Water masses probably act as physical barriers limiting the dispersal and controlling the diversity of bacteria in the ocean. Consequently, marine microbial biogeography involves more than geographical distances, as it is also dynamically associated with oceanic processes. Our ocean scale study suggests that it is essential to consider the coupling between microbial and physical oceanography to fully understand the diversity and function of marine microbes.
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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 | 0.001 |
| 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".