Structural sensitivity in the functional responses of predator-prey models

TL;DR

This paper investigates how different functional responses in predator-prey models can lead to significantly different bifurcation behaviors, affecting ecological predictions, and proposes methods to reduce this structural sensitivity for more accurate modeling.

Contribution

It extends the concept of structural sensitivity to the Leslie-Gower-May model and introduces stochasticity and analysis methods to identify key functional response features influencing bifurcations.

Findings

Adding stochasticity improves bifurcation similarity.

Prey densities near coexistence are crucial for bifurcation behavior.

Proposed procedures can enhance model prediction accuracy.

Abstract

In mathematical modeling, several different functional forms can often be used to fit a data set equally well, especially if the data is sparse. In such cases, these mathematically different but similar looking functional forms are typically considered interchangeable. Recent work, however, shows that similar functional responses may nonetheless result in significantly different bifurcation points for the Rosenzweig-MacArthur predator-prey system. Since the bifurcation behaviours include destabilising oscillations, predicting the occurrence of such behaviours is clearly important. Ecologically, different bifurcation behaviours mean that different predictions may be obtained from the models. These predictions can range from stable coexistence to the extinction of both species, so obtaining more accurate predictions is also clearly important for conservationists. Mathematically, this difference in bifurcation structure given similar functional responses is called structural sensitivity. We extend the existing work to find that the Leslie-Gower-May predator-prey system is also structurally sensitive to the functional response. Using the Rosenzweig-MacArthur and Leslie-Gower-May models, we then aim to determine if there is some way to obtain a functional description of data such that our model is not structurally sensitive. We first add stochasticity to the functional responses and find that better similarity of the resulting bifurcation structures is achieved. Then, we analyze the functional responses using two different methods to determine which part of each function contributes most to the observed bifurcation behaviour. We find that prey densities around the coexistence steady state are most important in defining the functional response. Lastly, we propose a procedure for ecologists and mathematical modelers to increase the accuracy of model predictions in predator-prey systems.