Mesopredators change temporal activity in response to a recolonizing apex predator
Bibliographic record
Abstract
Abstract Apex predators can influence ecosystems through density and behaviorally mediated effects on herbivores and mesopredators. In many parts of the world, apex predators live in, or are returning to, landscapes that have been modified by people; so, it is important to understand their ecological role in anthropogenic landscapes. We used motion-activated game cameras to compare the activity patterns of humans and 2 mesopredators, coyotes (Canis latrans) and bobcats (Lynx rufus), in areas with and without an apex predator, the gray wolf (Canis lupus), in a multiuse landscape of the northwestern United States. In areas with wolves, there was a significant increase in temporal niche overlap between the mesopredators owing to higher levels of coyote activity at all time periods of the day. Temporal overlap between mesopredators and humans also increased significantly in the presence of wolves. Coyotes exposed to wolves increased their activity during dawn, day, and dusk hours. The increase in coyote activity was greatest during the day, when wolves were least active. The direction of change in bobcat activity in areas with wolves was opposite to coyotes, suggesting a behaviorally mediated cascade between wolves, coyotes, and bobcats, although these findings would need to be confirmed with further research. Our findings suggest that mesopredators in human-dominated systems may perceive humans as less dangerous than apex predators, that humans may be more likely to encounter mesopredators in areas occupied by top predators, and that behaviorally mediated effects of apex predators on mesopredators persist in human-dominated landscapes.
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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 | 0.001 | 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) | 0.004 | 0.003 |
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; both teacher heads agree on what is shown here.
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".