Biogeographic gradients of picoplankton diversity indicate increasing dominance of prokaryotes in warmer Arctic fjords
Bibliographic record
Abstract
Climate change is opening the Arctic Ocean to increasing human impact and ecosystem changes. Arctic fjords, the region's most productive ecosystems, are sustained by a diverse microbial community at the base of the food web. Here we show that Arctic fjords become more prokaryotic in the picoplankton (0.2-3 µm) with increasing water temperatures. Across 21 fjords, we found that Arctic fjords had proportionally more trophically diverse (autotrophic, mixotrophic, and heterotrophic) picoeukaryotes, while subarctic and temperate fjords had relatively more diverse prokaryotic trophic groups. Modeled oceanographic connectivity between fjords suggested that transport alone would create a smooth gradient in beta diversity largely following the North Atlantic Current and East Greenland Current. Deviations from this suggested that picoeukaryotes had some strong regional patterns in beta diversity that reduced the effect of oceanographic connectivity, while prokaryotes were mainly stopped in their dispersal if strong temperature differences between sites were present. Fjords located in high Arctic regions also generally had very low prokaryotic alpha diversity. Ultimately, warming of Arctic fjords could induce a fundamental shift from more trophic diverse eukaryotic- to prokaryotic-dominated communities, with profound implications for Arctic ecosystem dynamics including their productivity patterns.
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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 | 0.000 | 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".