Density‐dependent prey behaviours and mutable predator foraging modes induce Allee effects and over‐prediction of prey mortality rates
Bibliographic record
Abstract
Abstract Predator–prey models are often used to represent consumptive interactions between species but, typically, are derived using simple experimental systems with little plasticity in prey or predator behaviours. However, many prey and predators exhibit a broad suite of behaviours. Here, we experimentally tested the effect of density‐dependent prey and predator behaviours on per capita relative mortality rates using Florida bass ( Micropterus floridanus ) consuming juvenile Bluegill ( Lepomis macrochirus ). Experimental ponds were stocked with a factorial design of low, medium, and high prey and predator densities. Prey mortality, prey–predator behaviours, and predator stomach contents were recorded over or after 7 days. We assumed the mortality dynamics followed foraging arena theory. This pathologically flexible predator–prey model separates prey into invulnerable and vulnerable pools where predators can consume prey in the latter. As this approach can represent classic Lotka–Volterra and ratio‐dependent dynamics, we fit a foraging arena predator–prey model to the number of surviving prey. We found that prey exhibited density‐dependent prey behaviours, hiding at low densities, shoaling at medium densities, and using a provided refuge at high densities. Predators exhibited ratio‐dependent behaviours, using an ambush foraging mode when one predator was present, hiding in the shadows at low prey–high predator densities, and shoaling at medium and high prey–high predator densities. The foraging arena model predicted the mortality rates well until the high prey–high predator treatment where group vigilance prey behaviours occurred and predators probably interfered with one another resulting in the model predicting higher mortality than observed. This is concerning given the ubiquity of predator–prey models in ecology and natural resource management. Furthermore, as Allee effects engender instability in population regulation, it could lead to inaccurate predictions of conservation status, population rebuilding or harvest rates.
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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 | 0.000 | 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 | 0.001 |
| 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".