Relationships Between Migration and Microbiome Composition and Diversity in Urban Canada Geese
Bibliographic record
Abstract
Microbiome analysis presents an opportunity to understand how urban environments affect avian physiology. For example, habitat use can affect microbiome diversity and composition, and hosts with more diverse gut microbiota are thought to be more resistant to pathogens and have increased fitness. However, the microbiome is an understudied aspect of avian ecology, particularly in the context of migration and urbanization in wild birds. For this study, we hypothesized that, within urban birds, migrants would exhibit greater microbial diversity and inter-individual variation in microbiome composition than residents because they are exposed to more diverse habitats. We focused on Canada geese ( Branta canadensis ), one of many migratory species that exhibit increasingly more year-round residency in cities. We used 16S rRNA gene amplicon sequencing to quantify microbiome taxonomic composition in fecal samples from 32 GPS-tracked Canada geese, 22 of which were year-round residents of the Chicago area and 10 of which were migrants. Similar to recent studies on wild species feeding near human habitation, urban resident geese had higher gut microbial diversity than migrants. They also had increased inter-individual variation in microbiome composition and, on average, lower relative abundances of bacteria in the phylum Firmicutes, and the genera Terrisporobacter , Turicibacter , and Cellulosilyticum , which all have metabolic functions that may aid in goose digestion. Therefore, the gut microbiome of resident geese may provide fewer potential health benefits. These patterns may be a result of anthropogenic influences on aspects of resident goose ecology, such as diet, as well the influence of migration on migrant goose ecology and biology. Overall, our results suggest that reduced migration for urban-adapted wildlife species may have important consequences for physiology and health.
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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".