The global distribution and climate resilience of marine heterotrophic prokaryotes
Bibliographic record
Abstract
Heterotrophic Bacteria and Archaea (prokaryotes) are a major component of marine food webs and global biogeochemical cycles. Yet, there is limited understanding about how prokaryotes vary across global environmental gradients, and how their global abundance and metabolic activity (production and respiration) may be affected by climate change. Using global datasets of prokaryotic abundance, cell carbon and metabolic activity we reveal that mean prokaryotic biomass varies by just under 3-fold across the global surface ocean, while total prokaryotic metabolic activity increases by more than one order of magnitude from polar to tropical coastal and upwelling regions. Under climate change, global prokaryotic biomass in surface waters is projected to decline ~1.5% per °C of warming, while prokaryotic respiration will increase ~3.5% ( ~ 0.85 Pg C yr−1). The rate of prokaryotic biomass decline is one-third that of zooplankton and fish, while the rate of increase in prokaryotic respiration is double. This suggests that future, warmer oceans could be increasingly dominated by prokaryotes, diverting a growing proportion of primary production into microbial food webs and away from higher trophic levels as well as reducing the capacity of the deep ocean to sequester carbon, all else being equal. This study uses global datasets of marine prokaryotes to reveal that prokaryotic biomass varies by just under 3-fold across the global surface ocean, while metabolic activity increases by more than one order of magnitude from polar to tropical coastal and upwelling regions. The findings also suggest that shifts under climate change could lead to an increasingly microbial-dominated ocean.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".