Host species and habitat shape fish-associated bacterial communities: phylosymbiosis between fish and their microbiome
Bibliographic record
Abstract
BACKGROUND: While many studies have reported that the structure of the gut and skin microbiota 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 (hereafter referred to as microbiota) and assess the extent to which host habitat and phylogeny predict microbiota 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 bacterial community composition using 16S metabarcoding and tested for community variation. RESULTS: We found that the water microbiota was distinct from the fish microbiota, although the skin microbiota more closely resembled the water microbiota. We also found that environmental (sample location), habitat, fish diet, and host species factors shape and promote divergence or convergence of the fish microbiota. Since host species significantly affected both gut and skin microbiota (separately from host species effects), we tested for phylosymbiosis using pairwise host species phylogenetic distance versus bacterial community dissimilarity. We found significant phylogenetic effects on bacterial community dissimilarity, consistent with phylosymbiosis for both the fish skin and gut microbiota, perhaps reflecting the longstanding co-evolutionary relationship between the host species and their microbiomes. CONCLUSIONS: Analyzing the gut and skin mucus microbiota 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. Video Abstract.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".