Ecophysiological analysis reveals distinct environmental preferences in closely related Baltic Sea picocyanobacteria
Bibliographic record
Abstract
Abstract Cluster 5 picocyanobacteria significantly contribute to primary productivity in aquatic ecosystems. Estuarine populations are highly diverse and consist of many co‐occurring strains, but their physiology remains largely understudied. In this study, we characterized 17 novel estuarine picocyanobacterial strains. Phylogenetic analysis of the 16S rRNA and pigment genes ( cpcB and cpeBA ) uncovered multiple estuarine and freshwater‐related clusters and pigment types. Assays with five representative strains (three phycocyanin rich and two phycoerythrin rich) under temperature (10–30°C), light (10–190 μmol photons m −2 s −1 ), and salinity (2–14 PSU) gradients revealed distinct growth optima and tolerance, indicating that genetic variability was accompanied by physiological diversity. Adaptability to environmental conditions was associated with differential pigment content and photosynthetic performance. Amplicon sequence variants at a coastal and an offshore station linked population dynamics with phylogenetic clusters, supporting that strains isolated in this study represent key ecotypes within the Baltic Sea picocyanobacterial community. The functional diversity found within strains with the same pigment type suggests that understanding estuarine picocyanobacterial ecology requires analysis beyond the phycocyanin and phycoerythrin divide. This new knowledge of the environmental preferences in estuarine picocyanobacteria is important for understanding and evaluating productivity in current and future ecosystems.
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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) | 0.000 | 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".