Canada goose fecal microbiota correlate with geography more than host‐associated factors
Bibliographic record
Abstract
Gut microbiota interact with host biology in numerous important ways. The forces shaping the composition, diversity, and function of the microbiota vary within and between species. Avian microbiota often correlate more strongly with sampling location specific environmental variables than with host‐associated factors such as age, but robust, range‐wide sampling is rare. To better understand the connection between geographic distance and the microbiota, fecal samples were collected from non‐migratory Canada goose populations across the United States. We expected that geographically closer populations would be exposed to more similar environmental microbes and would therefore have more similar gut microbiota. We hypothesized that intrinsic host‐associated factors would have a weak correlation to gut microbial composition and geographic distance would have a stronger correlation. We found that some components of Canada goose microbiota are present in a majority of the geese, including four bacterial phyla, five families, and three genera. However, there were significant differences in microbial alpha diversity based on state of origin as well as significant positive correlations between geography and beta diversity. Supervised machine learning models were able to predict the state and flyway of origin of a fecal sample based on bacterial composition alone. Distance−decay analysis showed a significant positive relationship between geographic distance and beta diversity. Our work provides novel insights into the microbiota of the Canada goose and supports the hypothesis that avian microbiota are influenced by the host's environment. This work also suggests that there is a minimum geographic distance, likely associated with sufficient variation in habitat, climate, and local food sources, that must be reached before significant differences in the microbiota between two populations can be detected.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".