Understanding the impacts of extreme weather on biological control through traveling wave analysis of a prey-predator model
Bibliographic record
Abstract
This study investigates the effects of extreme weather events on the efficacy of biological control of invasive species using a prey-predator reaction-diffusion model with the Allee effect. The model incorporates weather and environmental factors that affect species mortality, growth, and interaction rates. We first derive exact traveling wave solutions of the reduced model using the generalized exponential rational function method. The stability of the wave solutions is then numerically confirmed by demonstrating that the model solutions, perturbed by various levels of noise in the initial condition, converge to the traveling wave solutions. This suggests that extreme weather events that only impact the initial population sizes have only transient effects and do not change the fate of the species. Nevertheless, asymptotic analysis of the full model reveals conditions for the existence of bistable traveling wave solutions. This implies that extreme weather events may lead to predator extinction and the subsequent establishment of prey in a spatial domain. Hence, extreme weather events may lead to failure of biological control efforts and the persistence of invasive species. In addition, we establish conditions under which extreme weather events drive both prey and predator populations to extinction, which would be an ideal outcome for biological control of invasive species. This study highlights the importance of refining widely accepted ecological models to enhance the forecasting of ecological outcomes under extreme weather events.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".