Adaptive strategies of high and low nucleic acid prokaryotes in response to declining resource availability and selective grazing by protozoa
Bibliographic record
Abstract
Abstract Prokaryotes play a fundamental role in global ocean biogeochemical cycles. However, how the abundance and metabolic activity of ecologically distinct subgroups (i.e., high nucleic acid (HNA) and low nucleic acid (LNA) cells), and their regulating factors, change in response to changing marine environmental conditions remains poorly understood. Here, we delved into the time-evolving dynamic responses of the HNA and LNA prokaryotic subgroups to declining resource availability and selective grazing by protozoa by conducting a 73-day incubation experiment in a large-volume (117,000 L) macrocosm that facilitates community-level exploration. We found that the metabolic activity of the HNA subgroup was higher than that of the LNA subgroup when the macrocosm was resource replete but that the HNA subgroup declined more rapidly than the LNA subgroup as the resources became increasingly scarce, leading to a steadily increasing contribution of LNA cells to prokaryotic activity. Meanwhile, as resources in the macrocosm became limited, protozoan grazing preference shifted from the HNA to the LNA subgroup and the contributions of the LNA subgroup to the carbon flow within the macrocosm increased. The findings highlight the resilience of LNA cells in resource-limited environments, illuminate the critical role of selective grazing by protozoa in balancing distinct prokaryotic subgroups under changing resource conditions, and demonstrate the complex and adaptive interactions between protozoa and prokaryotes across diverse environmental contexts.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".