Geography, seasonality, and host‐associated population structure influence the fecal microbiome of a genetically depauparate Arctic mammal
Bibliographic record
Abstract
The Canadian Arctic is an extreme environment with low floral and faunal diversity characterized by major seasonal shifts in temperature, moisture and daylight. Muskoxen (Ovibos moschatus) are one of few large herbivores able to survive this harsh environment. Microbiome research of the gastrointestinal tract may hold clues as to how muskoxen exist in the Arctic, but also how this species may respond to rapid environmental changes. In this study, we investigated the effects of season (spring/summer/winter), year (2007-2016), and host genetic structure on population-level microbiome variation in muskoxen from the Canadian Arctic. We utilized 16S rRNA gene sequencing to characterize the fecal microbial communities of 78 male muskoxen encompassing two population genetic clusters. These clusters are defined by Arctic Mainland and Island populations, including; 1) two mainland sampling locations of the Northwest Territories and Nunavut; and 2) four locations of Victoria Island. Between these geographic populations, we found that differences in the microbiome reflected host-associated genetic cluster with evidence of migration. Within populations, seasonality influenced bacterial diversity with no significant differences between years of sampling. We found evidence of pathogenic bacteria, with significantly higher presence in mainland samples. Our findings demonstrate the effects of seasonality and the role of host population-level structure in driving fecal microbiome differences in a large Arctic mammal.
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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 | 0.001 | 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".