Use of visual and olfactory sensory cues by an apex predator in deciduous forests
Bibliographic record
Abstract
Predator–prey interactions influence behaviors and life-history evolution for both predator and prey species and also have implications for biodiversity conservation. A fundamental goal of ecology is to clarify mechanisms underlying predator–prey interactions and dynamics. To investigate the role of predator sensory mechanisms in predator–prey interactions, specifically in predator detection of prey, we experimentally evaluated importance of visual and olfactory cues for an apex predator, the coyote (Canis latrans Say, 1823). Unlike similar studies, we examined use of sensory cues in a field setting. We used trail cameras and four replicated treatments — visual only, olfactory only, visual and olfactory combined, and a control — to quantify coyote visitation rates in North American deciduous forests during fall 2016. Coyote visitation was greatest for olfactory-only and visual-only cues, followed by the combined olfactory–visual cue; all cues attracted more coyotes than the control (i.e., olfactory = visual > olfactory–visual > control). Our results suggest this apex predator uses both olfactory and visual cues while foraging for prey. These findings from a field study of free-roaming coyotes increase understanding of predator foraging behavior, predator–prey interactions, and sensory ecology. Our study also suggests future directions for field evaluations of the role of different sensory mechanisms in predator foraging and prey concealment behaviors.
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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 | 0.000 |
| 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 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".