Defining the ecological strategies of phytoplankton associated bacteria
Bibliographic record
Abstract
Ecological interactions between phytoplankton and bacteria govern the productivity and biogeochemistry of aquatic ecosystems. However, our understanding of these partnerships primarily comes from laboratory-based model systems, meaning that little is known about the establishment and dynamics of these interactions. Here, we tracked the development of the microbiome of 15 newly isolated phytoplankton species for 400 days. After only 20 days, each phytoplankton harboured a unique bacterial assemblage. Within these assemblages, we identify (i) specialist phytoplankton associates, which are bacteria that establish long-term interactions with 1-2 phytoplankton strains; (ii) generalists, which are associated with 3 or more phytoplankton strains; and (iii) transients, which do not develop sustained associations with any phytoplankton strains. Compared to transients, the generalists and specialists are enriched in genes involved in chemotaxis, vitamin synthesis, secondary metabolite production and the ability to uptake specific phytoplankton-derived compounds. Moreover, generalists display greater potential to move between hosts and release antimicrobials. Finally, examination of co-occurrence patterns in oceanographic time-series revealed that generalists and specialists mirror their phytoplankton partner’s abundance in the environment. The divergent genomic characteristics of these discrete bacterial categories highlight different ecological strategies that likely shape phytoplankton microbiomes. Phytoplankton-bacteria interactions regulate the health of marine ecosystems; however, how these interactions are established remains unclear. Here, the authors isolate 15 phytoplankton strains and track the dynamics of their microbiome, identifying specialist and generalist bacteria and the genomic traits enabling these distinct partnerships.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".