Inter-population differences in coyote diet and niche width along an urban–suburban–rural gradient
Bibliographic record
Abstract
Abstract Many generalist species thrive in urban environments by supplementing their diets with anthropogenic food, which creates numerous challenges for managing urban wildlife. Management could be advanced with more information on how spatial and temporal variation in habitat use by urban animals predicts variation in their dietary ecology. In this study, we used stable isotope analysis complemented with GPS collar location data to determine how diet composition and the dietary niche of coyotes (Canis latrans) varied across a sample of 169 individuals collected along an urban-to-rural gradient in Alberta, Canada. We further categorized urban individuals as either matrix (frequent use of developed areas) or greenspace (use of natural areas) via GPS locations. Matrix coyotes were isotopically distinct from all other coyote populations: they had the largest dietary niche, exhibited the most among-individual variation in diet, consumed the most anthropogenic food and fruit, and consumed the least amount of prey. Greenspace coyotes consumed more anthropogenic food than rural and suburban coyotes but otherwise exhibited similar niche width, among-individual heterogeneity, and prey consumption. We further tested for seasonal dietary variation and found that urban coyotes had a larger dietary niche during the summer, when they also consumed more anthropogenic food. Our conclusions were robust to our choice of mixing model parameters, including discrimination factors, suggesting that these methodological choices have limited effect when discerning relative trends among populations. Overall, our results suggest that management of urban coyotes should target the food sources accessible to coyotes in the urban matrix to reduce human–coyote conflict.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".