Faecal microbiota and antibiotic resistance genes in migratory waterbirds with contrasting habitat use
Bibliographic record
Abstract
Migratory birds may have a vital role in the spread of antimicrobial resistance across habitats and regions, but empirical data remain scarce. We investigated differences in the gut microbiome composition and the abundance of antibiotic resistance genes (ARGs) in faeces from four migratory waterbirds wintering in South-West Spain that differ in their habitat use. The white stork Ciconia ciconia and lesser black-backed gull Larus fuscus are omnivorous and opportunistic birds that use highly anthropogenic habitats such as landfills and urban areas. The greylag goose Anser anser and common crane Grus grus are herbivores and use more natural habitats. Fresh faeces from 15 individuals of each species were analysed to assess the composition of bacterial communities using 16S rRNA amplicon-targeted sequencing, and to quantify the abundance of the Class I integron integrase gene (intI1) as well as genes encoding resistance to sulfonamides (sul1), beta-lactams (blaTEM, blaKPC and blaNDM), tetracyclines (tetW), fluoroquinolones (qnrS), and colistin (mcr-1) using qPCR. Bacterial communities in gull faeces were the richest and most diverse. Beta diversity analysis showed segregation in faecal communities between bird species, but those from storks and gulls were the most similar, these being the species that regularly feed in landfills. Potential bacterial pathogens identified in faeces differed significantly between bird species, with higher relative abundance in gulls. Faeces from birds that feed in landfills (stork and gull) contained a significantly higher abundance of ARGs (sul1, blaTEM, and tetW). Genes conferring resistance to last resort antibiotics such as carbapenems (blaKPC) and colistin (mcr-1) were only observed in faeces from gulls. These results show that these bird species are reservoirs of antimicrobial resistant bacteria and suggest that waterbirds may disseminate antibiotic resistance across environments (e.g., from landfills to ricefields or water supplies), and thus constitute a risk for their further spread to wildlife and humans.
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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 | 0.002 |
| 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".