Measuring the population-level consequences of predator-induced prey movement
Bibliographic record
Abstract
Questions: (1) What impact does adaptive movement away from areas of high predation risk have on the dynamics of a prey species and its resource? (2) What can experiments that introduce or remove predator cues tell us about the answer to question (1)? Mathematical methods: These questions are addressed using a two-patch meta-community model in which predators and/or their cues are incorporated into a system consisting of a prey species and its resource. Predators and/or cues may be introduced to one or both patches. Key assumptions: Prey species move adaptively to maximize their instantaneous rate of increase, but also make some random movement. Predators move randomly or do not move. Resources seldom or never move between patches. Consumer species have saturating functional responses. Conclusions: (1) Adaptive movement can stabilize or destabilize the dynamics of the tri-trophic system. (2) Monitoring densities in a single patch may give a misleading indication of the global change in densities. (3) Adaptive prey movement in response to predator cues may increase or decrease prey density. (4) Predator introduction may cause an increase or decrease in the size of the prey population. (5) Short-term experiments with local measurements may greatly overestimate the impact of predators on prey and the behavioural component of that impact. (6) The dynamics and interspecific effects in a system with predators and adaptive avoidance by prey cannot in general be deduced from separate experiments with cues alone and with predators in the absence of the cues. (7) Conclusions from recent empirical studies should be reassessed in light of these results.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".