Microscopic marine invertebrate microbiomes do not reveal signatures of phylosymbiosis
Bibliographic record
Abstract
Animals and microorganisms often establish close ecological relationships. However, much of our knowledge on animal microbiomes comes from two deeply studied groups: vertebrates and arthropods. To understand interactions on a broader scale of diversity, we characterized the bacterial microbiomes of close to 1,000 microscopic marine invertebrates from 21 phyla, covering most of the remaining tree of metazoans. Samples were collected from five temperate and tropical locations covering three marine habitats (sediment, water column and intertidal macroalgae) and bacterial microbiomes were characterized using 16S rRNA gene sequencing. Our data show that, despite their size, these animals harbour bacterial communities that differ from those in the surrounding environment. Distantly related but coexisting invertebrates tend to share many of the same bacteria, suggesting that guilds of microorganisms preferentially associated with animals, but not tied to any specific host lineage, are the main drivers of the ecological relationship. Host identity is a minor factor shaping these microbiomes, which do not show the kind of correlation with host phylogeny, or “phylosymbiosis”, observed in many large animals. Hence, the current debate on the varying strength of phylosymbiosis within selected lineages should be reframed to account for the possibility that such a pattern might be the exception rather than the rule.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.039 |
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".