Effects of spatiotemporal, temporal and spatial nonlocal prey competitions on population distributions for a prey-predator system with generalist predation
Bibliographic record
Abstract
Conventional wisdom suggests that a prey-predator system with a generalist predator exhibits more stable dynamics than with a specialist predator. However, recent developments show that the presence of a generalist predator can lead to comparatively complex dynamics, including bistability, tristability, and several local as well as global bifurcations. In this paper, we study the dynamics of both local and nonlocal models of prey-predator interactions with generalist-type predation. Nonlocal intra-specific prey competition is assumed to be spatiotemporal, purely temporal, or purely spatial in nature. Also, we primarily aim to understand the resulting system dynamics under conditions of subpar and limited substitute food options available to the generalist predator. We first ensure that the local model is well-posed, and then provide the conditions for the existence and non-existence of spatially heterogeneous steady state solutions by using the maximum principle, Poincaré inequality and Leray-Schauder degree theory. Further, we derive the conditions for Turing instability in both the local and nonlocal models by using the linear analysis. We then illustrate a wide class of stationary and dynamic patterns obtained through numerical simulations for all the considered models, where the choice of the parametric domain is partially guided by the analytical results. This study reveals that the nonlocal model with purely spatial kernel admits spatial-Hopf bifurcation which gives rise to population oscillations around a 'ghost attractor', whereas this phenomenon does not occur in the other models.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".