Individual and Temporal Variation in Use of Residential Areas by Urban Coyotes
Bibliographic record
Abstract
Coyotes ( Canis latrans ) have established populations in most major urban centers across North America. While the risk of attacks on humans or their pets is low, the presence of carnivores in areas with high human use has resulted in increased public concern. Having a clearer understanding of which animals are more likely to interact with humans and when interactions are more likely to occur will help mitigate human-carnivore conflicts. Despite clear broad-scale patterns of human avoidance, human-coyote interactions occur most frequently in residential areas. Our purpose was to determine if use of residential areas varied consistently across individuals or time. We used locations from GPS collars deployed on 14 coyotes in the Greater Toronto Area, Ontario, Canada from 2012 to 2017 to fit a step selection function. Average (±SE) home range size estimates were 17.3 ± 4.6 km 2 for resident coyotes and 102.8 ± 32.9 km 2 for non-residents. We found that coyotes used natural areas more (β = 0.07, SE = 0.02, p < 0.0001), and roads (β = −0.50, SE = 0.13, p < 0.0001) and residential areas (β = −0.79, SE = 0.21, p = 0.0001) less during the day than at night. We also found that coyotes were more likely to use residential areas in the breeding season from January to April (β = 0.69, SE = 0.20, p = 0.0007) and the pup rearing season from May to August (β = 0.54, SE = 0.13, p < 0.0001) than in the dispersal season from September to December. Lastly, we found that resident coyotes were less likely to use residential areas than non-resident coyotes (β = −1.13, SE = 0.26, p < 0.0001). As far as we are aware, our study is the first to identify the seasons when coyotes are more likely to use residential areas. The seasonal patterns in habitat use that we observed reflect patterns that have been previously reported for human-coyote conflicts. Our results demonstrate that reducing the availability of anthropogenic food sources in residential areas, particularly in the winter and spring, should be a priority for managers aiming to reduce human-coyote conflict in urban areas.
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 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".