Gut microbiome composition predicts summer core range size in two divergent ungulates
Bibliographic record
Abstract
The gut microbiome of animals vary by age, diet, and habitat, and directly influences an individual's health. Similarly, variation in home ranges is linked to feeding strategies and fitness. Ungulates (hooved mammals) exhibit species-specific microbiomes and habitat use patterns. We combined gut microbiome and movement data to assess relationships between space use and the gut microbiome in a specialist and a generalist ungulate. We GPS radiocollared 24 mountain goats (Oreamnos americanus) and 34 white-tailed deer (Odocoileus virginianus), collected fecal samples, and conducted high-throughput sequencing of the 16S rRNA gene. We generated gut diversity metrics and key bacterial ratios. Our research question centred around the idea that larger Firmicutes to Bacteroidetes ratios confer body size or fat advantages that allow for larger home ranges, and relationships of disproportionate habitat use are stronger in the habitat specialist mountain goat. Firmicutes to Bacteroidetes ratios were positively correlated with core range area in both species. Mountain goats exhibited a negative relationship between gut diversity and proportional use of treed areas and escape terrain, and no relationships were detected in the habitat generalist white-tailed deer. This is the first study to relate range size to the gut microbiome in wild ungulates and is an important proof of concept that advances the information that can be gleaned from non-invasive sampling.
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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 | 0.000 | 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".