Sampling Microbial Dynamics in the Salish Sea Estuary: Evaluating Methods to Capture Cyanobacteria and Cyanophage
Bibliographic record
Abstract
Abstract Picocyanobacteria from the genera Prochlorococcus and Synechococcus thrive across the globe in aqueous environments, have relatively small genomes, and have growth dynamics regulated by both viral interactions and abiotic conditions, making them excellent model organisms for exploring host-pathogen coevolution. The Salish Sea, located in the Western coastal waters bordering the USA and Canada, is at the current northern boundary (defined by Prochlorococcus versus Synechococcus prevalence ratios) of the range of Prochlorococcus . Predictions suggest that this boundary will shift northward as warmer waters move northward, providing an excellent system to study host-pathogen dynamics and coevolution in a changing environmental context. In preparation for such studies, we developed and refined methods to sample and sequence cyanobacteria, cyanophages, and their abiotic environment. In addition to basic methodological questions focused on the physical sampling, filtering, viral precipitation, DNA extraction, and technical replicability, we explored how well our filtering and extraction protocols enrich for our main target, picocyanobacteria. The protocol described herein can successfully discriminate large-cell eukaryotic organisms, but size fractionation of picocyanobacteria appears to be affected by the presence of free DNA, multicellular structures, and abundant tycheposons. Our preferred final protocol at the conclusion of these experiments based on yield and processing time is presented. We recovered substantial Prochlorococcus, Synechococcus and amoeba-like sequences in most samples, and preliminary exploration of relative taxon sequence read recoveries across locations, over time, and tidal conditions are also discussed. Approaches described here may be useful to other efforts such as harmful algal bloom monitoring, species isolation and enrichment, water quality assessments, anti-viral discovery, and understanding picocyanobacterial population changes over space and time.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".