Fear of predators in free-living wildlife reduces population growth over generations
Bibliographic record
Abstract
Correctly assessing the total impact of predators on prey population growth rates (lambda, λ) is critical to comprehending the importance of predators in species conservation and wildlife management. Experiments over the past decade have demonstrated that the fear (antipredator responses) predators inspire can affect prey fecundity and early offspring survival in free-living wildlife, but recent reviews have highlighted the absence of evidence experimentally linking such effects to significant impacts on prey population growth. We experimentally manipulated fear in free-living wild songbird populations over three annual breeding seasons by intermittently broadcasting playbacks of either predator or nonpredator vocalizations and comprehensively quantified the effects on all the components of population growth, together with evidence of a transgenerational impact on offspring survival as adults. Fear itself significantly reduced the population growth rate (predator playback mean λ = 0.91, 95% CI = 0.80 to 1.04; nonpredator mean λ = 1.06, 95% CI = 0.96 to 1.16) by causing cumulative, compounding adverse effects on fecundity and every component of offspring survival, resulting in predator playback parents producing 53% fewer recruits to the adult breeding population. Fear itself was consequently projected to halve the population size in just 5 years, or just 4 years when the evidence of a transgenerational impact was additionally considered (λ = 0.85). Our results not only demonstrate that fear itself can significantly impact prey population growth rates in free-living wildlife, comparing them with those from hundreds of predator manipulation experiments indicates that fear may constitute a very considerable part of the total impact of predators.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".