Data from: Metabarcoding of fecal pellets in wild muskox populations reveals negative relationships between microbiome and diet alpha diversity
Bibliographic record
Abstract
Microbiome diversity and diet composition concomitantly influence species health, fitness, immunity, and digestion. In environments where diet varies spatially and temporally, microbiome plasticity may promote rapid host adaptation to available resources. For northern ungulates in particular, metabarcoding of noninvasively collected fecal pellets presents unprecedented insights into their diverse ecological requirements and niches by clarifying the interrelationships of microbiomes, key to deriving nutrients, in context of altered forage availability in changing climates. Muskoxen (Ovibos moschatus) are Arctic-adapted species that experience fluctuating qualities and quantities of vegetation. Geography and seasonality have been noted to influence microbiome composition and diversity in muskoxen, yet it is unclear how their microbiomes intersect with diet. Following observations from other species, we hypothesized increasing diet diversity would result in higher microbiome diversity in muskoxen. We assessed diet composition in muskoxen using three common plant metabarcoding markers and explored correlations with microbiome data. Patterns of dietary diversity and composition were not fully concordant among the markers used, yet all reflected the primary consumption of willows and sedges. Individuals with similar diets had more similar microbiomes, yet in contrast to most literature, yielded negative relationships between microbiome and diet alpha diversity. This negative correlation may reflect the unique capacities of muskoxen to survive solely on high-fiber Arctic forage and provide insight into their resiliency to exploit changing dietary resources in a rapidly warming Arctic altering vegetation diversity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".