Seasons Influence the Native Gut Microbiome of Lake Trout Salvelinus namaycush
Bibliographic record
Abstract
The fish gut microbiome plays an essential role in the host’s development and survival. Environmental factors can shape the gut microbiome and potentially mediate physiological performance. Seasonal environments that experience regular abiotic and biotic transitions likely drive variability in the gut microbiome. However, we know very little about how seasonal transitions interact with the gut microbiome, especially at northern latitudes where seasonality is exaggerated. Therefore, in this study, we analyzed the native gut microbiome of a flexible top predator fish species, the lake trout, across seasons in a boreal lake using 16S rRNA sequencing. Seasonal changes explained one third of the bacterial variance. The relative abundance of several bacterial taxa influenced the observed seasonal differences in the community; pathogenic Pseudomonas and Stenotrophomonas were most abundant in the spring and winter seasons, and chemoheterotrophic Macrococcus and Lelliottia were most abundant in the summer. The fall season was dominated by unclassified Clostridiaceae, potentially linked to a shift in lake trout foraging. Therefore, we present novel evidence that suggests seasonality is likely to shape the native gut microbiome of lake trout; however, the mechanistic links between the environment and the microbial diversity of the gut remain to be uncovered.
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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 | 0.000 | 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) | 0.001 | 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".