Population dynamical consequences of reduced predator switching at low total prey densities
Bibliographic record
Abstract
Abstract Several models of rapid switching by a predator in a two‐prey environment are analyzed. The goal is to determine how the dynamics of the system and the potential indirect effects between prey are affected by the dependence of switching on total prey density. In exploring this question, the difference between the population‐level consequences of switching in stable and cycling predator‐prey systems is also examined. We concentrate on reduced switching at low densities, a feature that is likely because of the difficulty of distinguishing between two very low densities. The main findings are: (1) switching in unstable systems can produce positive indirect effects of one prey species on the other; and (2) reduced switching at low densities can greatly alter the dynamics of the system and the indirect effects between prey. Both of the possibilities are only evident in cycling systems. Reduced switching at low total prey densities leads to heavier predation on the slower‐growing prey when both prey species are rare. As a consequence, there is a lag in the recovery of the slower‐growing prey species after predator densities fall, and the dynamics of the two prey become desynchronized. The net result is increased indirect interactions between prey, and a greater likelihood of exclusion of the slower growing prey. The analysis of these models suggests a need for more empirical work to determine whether switching is reduced by very low total prey densities, and to study the long‐term dynamics that occur in systems with switching predators.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".