Fearlessness towards extirpated large carnivores may exacerbate the impacts of naïve mesocarnivores
Bibliographic record
Abstract
By suppressing mesocarnivore foraging, the fear large carnivores inspire can be critical to mitigating mesocarnivore impacts. Where large carnivores have declined, mesocarnivores may quantitatively increase foraging, commensurate with reductions in fear. The extirpation of large carnivores may further exacerbate mesocarnivore impacts by causing qualitative changes in mesocarnivore behavior. Error management theory suggests that, where predators are present, prey should be biased towards over-responsiveness to predator cues, abandoning foraging in response to both predator cues and benign stimuli mistaken for predator cues (false-positives). Where predators are absent, prey may avoid these foraging costs by becoming unresponsive (naïve) to both predator cues and false-positives. If naiveté occurs in mesocarnivores where large carnivores have been extirpated, it could substantively exacerbate their impacts, as “fearless” mesocarnivores may engage in virtually unrestricted foraging. We tested the naiveté of raccoons (Procyon lotor) to extirpated large carnivores in the context of a larger experiment demonstrating that fear of large carnivores can mediate mesocarnivore impacts. Raccoon responsiveness to playbacks of their extirpated large carnivore predators (cougars, Puma concolor; bears, Ursus americanus) was significantly less than to the only extant large carnivore predator (dogs), and was no greater than to non-predators (“seals”; Phoca vitulina, Eumetopias jubatus). Raccoons failed to recognize their now extirpated predators as threatening, spending as much time foraging as when hearing non-predators, which we estimate has substantive impacts, based on results from the larger experiment. We discuss the potentially powerful role of “fearlessness” in exacerbating mesocarnivore impacts in systems where large carnivores have been lost.
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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 | 0.000 | 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) | 0.005 | 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".