A meta‐analysis revealing the technical, environmental, and host‐associated factors that shape the gut microbiota of Atlantic salmon and rainbow trout
Bibliographic record
Abstract
Abstract Salmonids, specifically Atlantic salmon (Salmo salar) and rainbow trout (Oncorhynchus mykiss), are commonly farmed and their gut microbiota plays important roles for optimal growth, health, and physiology. However, differences in experimental design, technical factors and bioinformatics make it challenging to compare the results from different studies and draw general conclusions about their influence on the fish gut microbiota. For a more comprehensive understanding of the gut microbiota, we collected all the publicly accessible 16S rRNA gene sequencing data with clearly stated sample metadata from freshwater Atlantic salmon and rainbow trout intestinal contents and mucosa sequenced on the Illumina MiSeq platform. A total of 783 samples from 19 published studies were included in this meta‐analysis to test the impact of the technical, environmental, and host‐accociated factors. This meta‐analysis revealed that all the tested factors significantly influenced the alpha and beta diversities of the gut microbiota of salmon and trout. Technical factors, especially target region and DNA extraction kit, affected the beta diversity to a larger extent, while host‐associated and environmental factors, especially diet and initial fish weight, had a higher impact on the alpha diversity. Salmon had a higher alpha diversity and higher abundance of Enterococcus and Staphylococcus than trout, which had higher abundance of Weissella and Mycoplasma. The results of this meta‐analysis fill in a critical knowledge gap that demonstrate technical methodologies must be standardized and factors associated with host and environment need to be accounted for in the future design of salmonid gut microbiota experiments.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".