Gut microbiome composition predicts summer core range size in a generalist and specialist ungulate
Bibliographic record
Abstract
Abstract The gut microbiome of animals varies by age, diet, and habitat, and directly influences individual health. Similarly, variation in an individual’s home range can lead to differences in feeding strategies and fitness. Ungulates (hooved mammals) exhibit species-specific microbiomes and habitat use patterns: here, 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 captured and GPS radiocollared 24 mountain goats ( Oreamnos americanus ) and 34 white-tailed deer ( Odocoileus virginianus ). We collected fecal samples and conducted high-throughput sequencing of the 16S rRNA gene. Using GPS data, we estimated core (50%) and home range (95%) sizes and calculated proportional use for several important habitat types. We generated metrics related to gut diversity and key bacterial ratios. We hypothesize that larger Firmicutes to Bacteroides ratios confer body size or fat advantages that allow for larger home ranges, and that relationships between gut diversity and disproportionate habitat use is stronger in mountain goats due to their restricted niche relative to white-tailed deer. Firmicutes to Bacteroides ratios were positively correlated with core range area in both species. Mountain goats exhibited a negative relationship between gut diversity and use of two key habitat types (treed areas and escape terrain), whereas no relationships were detected in white-tailed deer. This is the first study to relate core 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 | 0.001 | 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".