Biogeography of Heterotrophic Flagellate Populations Indicates the Presence of Generalist and Specialist Taxa in the Arctic Ocean
Bibliographic record
Abstract
Heterotrophic marine flagellates (HF) are ubiquitous in the world's oceans and represented in nearly all branches of the domain Eukaryota. However, the factors determining distributions of major taxonomic groups are poorly known. The Arctic Ocean is a good model environment for examining the distribution of functionally similar but phylogenetically diverse HF because the physical oceanography and annual ice cycles result in distinct environments that could select for microbial communities or favor specific taxa. We reanalyzed new and previously published high-throughput sequencing data from multiple studies in the Arctic Ocean to identify broad patterns in the distribution of individual taxa. HF accounted for fewer than 2% to over one-half of the reads from the water column and for up to 60% of reads from ice, which was dominated by Cryothecomonas. In the water column, many HF phylotypes belonging to Telonemia and Picozoa, uncultured marine stramenopiles (MAST), and choanoflagellates were geographically widely distributed. However, for two groups in particular, Telonemia and Cryothecomonas, some species level taxa showed more restricted distributions. For example, several phylotypes of Telonemia favored open waters with lower nutrients such as the Canada Basin and offshore of the Mackenzie Shelf. In summary, we found that while some Arctic HF were successful over a range of conditions, others could be specialists that occur under particular conditions. We conclude that tracking species level diversity in HF not only is feasible but also provides a potential tool for understanding the responses of marine microbial ecosystems to rapidly changing ice regimes.
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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 | 0.001 | 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".