MEASURING THE IMPACT OF DYNAMIC ANTIPREDATOR TRAITS ON PREDATOR–PREY–RESOURCE INTERACTIONS
Bibliographic record
Abstract
This article analyzes the limitations of the most widely used method for quantifying the impact of dynamic antipredator traits on food chain dynamics and discusses alternative approaches. The standard method for a predator-prey-resource chain estimates the effects of the prey's defensive behavior by comparing population densities or fitness measures in a "predator cue" treatment to those in a no-predator treatment. This design has been interpreted as providing a measure of the "nonconsumptive effect" of the predator on the prey and the "trait-mediated indirect effect" of the predator on the resource. Other approaches involve measurements of the impact of the behavior in the presence of functional predators. The questions addressed here are: (1) How consistent are the results of different approaches? (2) How time-dependent are their results? (3) How well do they correspond to theoretical measures of effect size? (4) How useful are the measurements in understanding system dynamics? A model of a tritrophic system in which the prey species adjusts a defensive trait adaptively is used to evaluate the experimental designs. Measures of changes in prey fitness or population density in a cue treatment generally include offsetting effects of the cost of the behavior and the benefit of more resources. This means that the sign of the effect, as well as its magnitude, may change depending on when the experiment is terminated. Because predation is not present in the cue treatment, few conclusions can be drawn about the impact of the behavior on population densities or fitness of the prey in a natural setting with predators. Cue experiments often do not accurately separate trait-mediated from density-mediated effects on the resource. Most scalar measures of effects are sensitive to experimental duration and initial densities. Use of a wider range of experimental designs to measure trait-related effects is called for.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".