State‐dependent risk‐taking by predators in systems with defended prey
Bibliographic record
Abstract
Even defended prey items may contain nutrients that can sustain predators in times of energetic need. Conversely, a well‐fed predator might be expected to avoid attacking prey items that have a chance of being defended, particularly if there is an abundance of familiar palatable prey to support it. To further understand the implications of optimal state‐dependent foraging behaviour by predators in systems that contain defended prey, I developed a stochastic dynamic programming model. This state‐dependent approach formally accounts for the trade‐off between avoiding starvation and minimising harm from attacking defended prey. It predicts that the mean attack probability of predators on defended models and their undefended mimics should decline in a sigmoidal fashion with increasing availability of alternative undefended prey, and that the foraging decisions of predators should in general be relatively insensitive to the probability that a potentially defended prey item is indeed defended. Some implications of these predictions are that conspicuous warning signals are more likely to evolve in systems that contain an abundance of alternative undefended prey, and that imperfect mimicry will provide almost complete protection to the mimic when predators are readily supported by alternative food sources. Somewhat surprisingly, increasing the density of nutritious undefended mimics while keeping the densities of all other prey types constant tended to decrease the attack rates of predators on encounter with mimics and their defended models. This increase in dietary conservatism arose because in these cases there would be more prey available to sustain the predator if it ever found itself critically low in energy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".