Ecology of the rare microbial biosphere of the Arctic Ocean
Bibliographic record
Abstract
Understanding the role of microbes in the oceans has focused on taxa that occur in high abundance; yet most of the marine microbial diversity is largely determined by a long tail of low-abundance taxa. This rare biosphere may have a cosmopolitan distribution because of high dispersal and low loss rates, and possibly represents a source of phylotypes that become abundant when environmental conditions change. However, the true ecological role of rare marine microorganisms is still not known. Here, we use pyrosequencing to describe the structure and composition of the rare biosphere and to test whether it represents cosmopolitan taxa or whether, similar to abundant phylotypes, the rare community has a biogeography. Our examination of 740,353 16S rRNA gene sequences from 32 bacterial and archaeal communities from various locations of the Arctic Ocean showed that rare phylotypes did not have a cosmopolitan distribution but, rather, followed patterns similar to those of the most abundant members of the community and of the entire community. The abundance distributions of rare and abundant phylotypes were different, following a log-series and log-normal model, respectively, and the taxonomic composition of the rare biosphere was similar to the composition of the abundant phylotypes. We conclude that the rare biosphere has a biogeography and that its tremendous diversity is most likely subjected to ecological processes such as selection, speciation, and extinction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".