Greater consumption of protein‐poor anthropogenic food by urban relative to rural coyotes increases diet breadth and potential for human–wildlife conflict
Bibliographic record
Abstract
Reports of encounters between people and generalist urban‐adapted carnivores are increasing around the world. In North America, coyotes Canis latrans are among the carnivores that appear to be especially capable of incorporating novel anthropogenic food types, including those found in cities. Consuming anthropogenic food may benefit coyotes by increasing their dietary diversity, but it may also lead to increased interactions and conflicts with humans. To test these hypotheses, we compared the diets of urban and rural coyotes from two urban and three rural sites spanning 32 200 km 2 in Alberta, Canada. We analyzed scat samples to calculate diet diversity at the level of both individuals (species per scat) and populations (Shannon index) and to determine the frequency of anthropogenic food consumption. We complemented this comparison with stable isotope analyses of hair samples taken from individual urban and rural coyotes that were or were not reported by the public for repeatedly visiting backyards and schoolyards during the day. Relative to rural coyotes, urban coyotes had more diverse diets at the level of both individuals and populations, consumed anthropogenic food more often, and animals less often, than rural coyotes. Although urban coyotes assimilated more anthropogenic food than the rural coyotes overall, the urban coyotes reported for conflict assimilated less protein and were more likely to be diseased. Our results suggest that processed anthropogenic food may contribute to the success of urban coyotes, but does not entirely correlate with conflict. Instead, some seemingly innocuous, but low‐protein food sources such as bird feeders, compost, and cultivated fruit trees may contribute disproportionately to encounters with people for coyotes and other urban‐adapted opportunistic carnivores.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".