Nonequilibrium dynamics in a noise-induced predator–prey model
Bibliographic record
Abstract
Understanding the dynamics of predator–prey systems in the presence of different environmental variability is crucial in ecology for forecasting population behaviour and ensuring ecosystem sustainability . It is a challenging aspect of studying spatio-temporal dynamics in the presence of environmental variability. We provide a noise-induced spatio-temporal predator–prey model to explore how temporal variability and spatial heterogeneity affect population dynamics. The multiplicative stochastic fluctuations in space and time are considered in the prey’s growth rate and predator’s death rate to capture the demographic noise in the ecosystem. We first examine the deterministic models by finding crucial parameters that influence the stability and dynamics of predator and prey populations, following their impact on spatio-temporal pattern formation. Using analytical tools and numerical simulations, we illuminate the mechanisms behind the observed dynamics and highlight the significance of demographic noise in generating ecological patterns. The numerical simulations show that the temporal variability introduced by noise leads to oscillations in population densities and alters the stability of the predator–prey system. Special attention is given to the spatio-temporal system when it fails to produce Turing patterns without noise, and the results show that linear demographic change can cause complex behaviours, such as self-organization, irregular oscillations, and nonequilibrium dynamics. Nevertheless, these findings broadly affect various ecological phenomena, including population persistence, species coexistence , long transients, and ecosystem resilience to demographic perturbations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".