16S rDNA-Based Metagenomic Analysis of Bacterial Diversity Associated With Two Populations of the Kleptoplastic Sea Slug<i>Elysia chlorotica</i>and Its Algal Prey<i>Vaucheria litorea</i>
Bibliographic record
Abstract
The molluscan sea slug Elysia chlorotica is best known for its obligate endosymbiosis with chloroplasts (= kleptoplasty) from its algal prey Vaucheria litorea and its ability to sustain itself photoautotrophically for several months. This unusual photosynthetic sea slug also harbors an array of undescribed bacteria, which may contribute to the long-term success of the symbiosis. Here, we utilized 16S rDNA-based metagenomic analyses to characterize the microbial diversity associated with two populations of E. chlorotica from Halifax, Nova Scotia, Canada, and from Martha's Vineyard, Massachusetts, USA. Animals were examined immediately after collection from their native environments, after being starved of their algal prey for several months, and after being bred in the laboratory (second-generation sea slugs) to characterize the effect of varying environmental and culturing conditions on the associated bacteria. Additionally, the microbiome of the algal prey, laboratory-cultured V. litorea, was analyzed to determine whether the laboratory-bred sea slugs obtained bacteria from their algal food source during development. Bacterial profiles varied between populations and among all conditions except for the F2 laboratory-bred samples, which were similar in diversity and abundance, but not to the algal microbiome. Alpha-, beta-, and gamma-proteobacteria dominated all of the samples along with Actinobacteria, Bacilli, Flavobacteria, and Sphingobacteria. Bacteria capable of polysaccharide digestion and photosynthesis, as well as putative nitrogen fixation, vitamin B(12) production, and natural product biosynthesis were associated with the sea slug and algal samples.
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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".