An altered microbiome in urban coyotes mediates relationships between anthropogenic diet and poor health
Bibliographic record
Abstract
Generalist species able to exploit anthropogenic food sources are becoming increasingly common in urban environments. Coyotes (Canis latrans) are one such urban generalist that now resides in cities across North America, where diseased or unhealthy coyotes are frequently reported in cases of human-wildlife conflict. Coyote health and fitness may be related to habitat use and diet via the gut microbiome, which has far-reaching effects on animal nutrition and physiology. In this study, we used stomach contents, stable isotope analysis, 16S rRNA gene amplicon sequencing, and measures of body condition to identify relationships among habitat use, diet, fecal microbiome composition, and health in urban and rural coyotes. Three distinct relationships emerged: (1) Urban coyotes consumed more anthropogenic food, which was associated with increased microbiome diversity, higher abundances of Streptococcus and Enterococcus, and poorer average body condition. (2) Conversely, rural coyotes harbored microbiomes rich in Fusobacteria, Sutterella, and Anaerobiospirillum, which were associated with protein-rich diets and improved body condition. (3) Diets rich in anthropogenic food were associated with increased abundances of Erysipelotrichiaceae, Lachnospiraceae, and Coriobacteriaceae, which correlated with larger spleens in urban coyotes. Urban coyotes also had an increased prevalence of the zoonotic parasite Echinococcus multilocularis, but there were no detectable connections between parasite infection and microbiome composition. Our results demonstrate how the consumption of carbohydrate-rich anthropogenic food by urban coyotes alters the microbiome to negatively affect body condition, with potential relationships to parasite susceptibility and conflict-prone behavior.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".