Host Species and Habitat Shape Fish- associated Bacterial Communities: Phylosymbiosis between Fish and their Microbiome
Bibliographic record
Abstract
Abstract BackgroundWhile many studies have reported that the structure of the gut and skin microbiome is driven by both species-specific and habitat-specific factors, the relative importance of host-specific versus environmental factors in wild vertebrates remains poorly understood. The aim of this study was to determine the diversity and composition of fish skin, gut, and surrounding water bacterial communities (BCs) and assess the extent to which host habitat and phylogeny predict BC similarity. Skin swabs and gut samples from 334 fish belonging to 17 species were sampled in three Laurentian Great Lakes (LGLs) habitats (Detroit River, Lake Erie, Lake Ontario). We also collected and filtered water samples at the time of fish collection. We analyzed BC composition using 16S metabarcoding and tested for community variation. ResultsWe found that the water BC was distinct from the fish microbiome BCs, although the skin BC more closely resembled the water BC. We also found that environmental (sample location), habitat, fish diet and host species factors shape and promote divergence or convergence of the fish BCs. Since host species significantly affected both gut and skin BC (separately from host species effects), we tested for phylosymbiosis using pairwise host species phylogenetic distance versus BC dissimilarity. We found significant phylogenetic effects on BC dissimilarity, consistent with phylosymbiosis for both the fish skin and gut BCs, perhaps reflecting the longstanding co-evolutionary relationship between the host species and their microbiomes. ConclusionsAnalyzing the gut content and skin mucus BCs across diverse fish species in complex natural ecosystems such as the LGLs provides insights into the potential for habitat and species-specific effects on the microbiome, and ultimately the health, of the host.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".