Gut microbiome is affected by inter-sexual and inter-seasonal variation in diet for thick-billed murres (Uria lomvia)
Bibliographic record
Abstract
The role of the gut microbiome is increasingly being recognized by health scientists and veterinarians, yet its role in wild animals remains understudied. Variations in the gut microbiome could be the result of differential diets among individuals, such as variation between sexes, across seasons, or across reproductive stages. We evaluated the hypothesis that diet alters the avian gut microbiome using stable isotope analysis (SIA) and 16S rRNA gene sequencing. We present the first description of the thick-billed murre (Uria lomvia) fecal microbiome. The murre microbiome was dominated by bacteria from the genus Catellicoccus, ubiquitous in the guts of many seabirds. Microbiome variation was explained by murre diet in terms of proportion of littoral carbon, trophic position, and sulfur isotopes, especially for the classes Actinobacteria, Bacilli, Bacteroidia, Clostridia, Alphaproteobacteria, and Gammaproteobacteria. We also observed differences in the abundance of bacterial genera such as Catellicoccus and Cetobacterium between sexes and reproductive stages. These results are in accordance with behavioural observations of changes in diet between sexes and across the reproductive season. We concluded that the observed variation in the gut microbiome may be caused by individual prey specialization and may also be reinforced by sexual and reproductive stage differences in diet.
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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".