Fixed vs. Random Temporal Predictability of Predation Risk: An Extension of the Risk Allocation Hypothesis
Bibliographic record
Abstract
Abstract Predation is a strong selective force acting on prey animals. Predation is by nature highly variable in time; however, this aspect of predation risk has traditionally been overlooked by behavioural ecologists. Lima and Bednekoff proposed the predation risk allocation hypothesis (RAH), predicting how temporal variation in predation risk drives prey antipredator behaviours. This model is based on the concept that prey adaptively allocate their foraging and antipredator efforts across high‐ and low‐risk situations, depending on the duration of high‐ vs. low‐risk situations and the relative risk associated with each of them. An unstudied extension of the RAH is the effect of predictability of predation risk. A predictable risk should lead to prey displaying minimal vigilance behaviours during predictable low‐risk periods and the strongest antipredator behaviours during risky periods. Conversely, an unpredictable predation risk should result in prey displaying constant vigilance behaviour, with suboptimal foraging rates during periods of safety but antipredator behaviours of lower intensity during periods of risk. We tested this extension of the RAH using convict cichlids exposed to high‐risk alarm cues at two frequencies of risk (1× vs. 3×) per day, on either a fixed or random schedule for 5 d. We then tested the fish for a response to high‐risk cues (alarm cues) and to low‐risk cues (disturbance resulting from the introduction of distilled water). Our study supports previous results on the effects of risk frequency and cue intensity on cichlid behaviour. We failed to show an effect of risk predictability on the behavioural responses of cichlids to high‐risk alarm cues, but predictability did influence responses to low‐risk cues. We encourage further studies to test the effect of predictability in other systems.
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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 | 0.001 | 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) | 0.000 | 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".