Large-scale experimental assessment of coyote behavior across urban and rural landscapes
Bibliographic record
Abstract
Carnivores must navigate the complexities of human modifications to their environment. Natural resources and biodiversity decline in urban areas, while people in rural areas often pose greater direct risk through actions such as hunting. To evaluate if carnivore populations adapt their behavior to local risks in rural and urban environments, we compared behavioral responses to novel objects in coyotes (Canis latrans). We placed an attractant at arrays of 30 camera-trap stations at 16 pairs of urban and rural field sites across the USA, with a novel object placed at half of the stations. Coyotes exhibited more cautious behavior and remained farther from the attractant at all sites with the novel object; however, urban coyotes got closer to the attractant than rural coyotes. There were few behavioral differences between urban and rural coyotes and none between eastern and western coyotes. Coyotes across the USA exhibit neophobic behavior but urban coyotes, especially western coyotes, are willing to take more risk (i.e., be closer to the attractant). The consistency in most metrics of coyote behavior suggest that solutions developed in one area could be universally useful. This study also demonstrates the effectiveness of a large, collaborative approach to studying broad-scale patterns in behavioral traits.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".