Fear of the human “super predator” far exceeds the fear of large carnivores in a model mesocarnivore
Bibliographic record
Abstract
The fear (perceived predation risk) large carnivores inspire in mesocarnivores can affect ecosystem structure and function, and loss of the “landscape of fear” large carnivores create adds to concerns regarding the worldwide loss of large carnivores. Fear of humans has been proposed to act as a substitute, but new research identifies humans as a “super predator” globally far more lethal to mesocarnivores, and thus presumably far more frightening. Although much of the world now consists of human-dominated landscapes, there remains relatively little research regarding how behavioral responses to humans affect trophic networks, to the extent that no study has yet experimentally tested the relative fearfulness mesocarnivores demonstrate in reaction to humans versus nonhuman predators. Badgers (Meles meles) in Britain are a model mesocarnivore insofar as they no longer need fear native large carnivores (bears, Ursus arctos; wolves, Canis lupus) and now perhaps fear humans more. We tested the fearfulness badgers demonstrated to audio playbacks of extant (dog) and extinct (bear and wolf) large carnivores, and humans, by assaying the suppression of foraging behavior. Hearing humans affected latency to feed, vigilance, foraging time, number of feeding visits, and number of badgers feeding. Hearing dogs and bears had far lesser effects on latency to feed, and hearing wolves had no effects. Our results indicate fear of humans evidently cannot substitute for the fear large carnivores inspire in mesocarnivores because humans are perceived as far more frightening, which we discuss in light of the recovery of large carnivores in human-dominated landscapes.
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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 | 0.001 |
| 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".