Modelling predation: Theoretical criteria and empirical evaluation of functional form equations for predator-prey systems
Why this work is in the frame
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Bibliographic record
Abstract
Correct modelling of relationships between predators and prey is crucial to ecological and population dynamics models. However, and despite a long-standing competition between ratio and prey-dependent models (and a few alternative intermediate forms) in the literature, most equations currently used to represent such relationships do not meet theoretical criteria for biological consistency. This research proposes a set of universally applicable criteria for all predation equations and shows that the most commonly used predation equations in the literature fail to meet these same criteria. We follow with a proposal for a new predation equation that does meet these criteria, which combines both prey and ratio-dependent concepts while giving reasonable predictions in the cases of both high predator or high prey densities. We show its empirical performance by applying the new equation, along with existing alternatives, to various experimental predation datasets from the literature. Results show that the new equation is not only more mathematically consistent than existing equations, but also performs more consistently empirically across different datasets from various ecological situations. This research is the first to propose a systematic set of criteria to evaluate predation equations and then to offer an equation that meets these criteria and also performs well both theoretically and empirically across datasets from a wide range of predation systems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it