Canada goose fecal microbiota correlate with geography more than host-associated co-factors
Bibliographic record
Abstract
ABSTRACT The gut microbiota has many positive effects on the host, but how the microbiota is shaped and influenced can vary greatly. These factors affect the composition, diversity, and function of host-associated microbiota; however, these factors vary greatly from organism to organism and clade to clade. The avian microbiota often correlates more with the sampling locations rather than host-associated co-factors. These correlations between location and microbiota often only include a few sampling locations within the species’ range. To better understand the connection between geographic distance and the microbiota, were collected from non-migratory Canada geese across the United States. We expected host-associated factors to have minimal effect on the microbiota and geese microbiota will be strongly correlated to geography. We hypothesized more proximal geese will be exposed to more similar environmental microbes and will have more similar microbiota. Canada geese microbiota are largely similar across the entire sampling range. Several bacterial taxa were shared by more than half of the geese. Four phyla were found in the majority of the samples: Firmicutes, Proteobacteria, Bacteroidetes , and Actinobacteria . Three genera were also present in the majority of the samples: Helicobacter, Subdoligranulum, and Faecalibacterium . There were minimal differences in alpha diversity with respect to age, sex, and flyway. There were significant correlations between geography and beta diversity. Supervised machine learning models were able to predict the location of a fecal sample based on taxonomic data alone. Distance decay analysis show a positive relationship between geographic distance and beta diversity. Our work provides novel insights into the microbiota of the ubiquitous Canada goose and further supports the claim that the avian microbiota is largely dominated by the host’s environment. This work also suggests that there is a minimum distance that must be reached before significant differences in the microbiota between two individuals can be observed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".