Quantifying fear effects on prey demography in nature
Bibliographic record
Abstract
In recent years, it has been argued that the effect of predator fear exacts a greater demographic toll on prey populations than the direct killing of prey. However, efforts to quantify the effects of fear have primarily relied on experiments that replace predators with predator cues. Interpretation of these experiments must consider two important caveats: (1) the magnitude of experimenter-induced predator cues may not be realistically comparable to those of the prey's natural sensory environment, and (2) given functional predators are removed from the treatments, the fear effect is measured in the absence of any consumptive effects, a situation which never occurs in nature. We contend that demographic consequences of fear in natural populations may have been overestimated because the intensity of predator cues applied by experimenters in the majority of studies has been unnaturally high, in some instances rarely occurring in nature without consumption. Furthermore, the removal of consumption from the treatments creates the potential situation that individual prey in poor condition (those most likely to contribute strongly to the observed fear effects via starvation or reduced reproductive output) may have been consumed by predators in nature prior to the expression of fear effects, thus confounding consumptive and fear effects. Here, we describe an alternative treatment design that does not utilize predator cues, and in so doing, better quantifies the demographic effect of fear on wild populations. This treatment substitutes the traditional cue experiment where consumptive effects are eliminated and fear is simulated with a design where fear is removed and consumptive effects are simulated through the experimental removal of prey. Comparison to a natural population would give a more robust estimate of the effect of fear in the presence of consumption on the demographic variable of interest. This approach represents a critical advance in quantifying the mechanistic pathways through which predation structures ecological communities. Discussing the merits of both treatments will motivate researchers to go beyond simply describing the existence of fear effects and focus on testing their true magnitude in wild populations and natural communities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".