Exploring the Dynamics of Lotka-Volterra Systems: Efficiency, Extinction Order, and Predictive Machine Learning

TL;DR

This paper investigates Lotka-Volterra ecological systems using connectance properties, revealing how trophic efficiency affects persistence, and employs machine learning models to predict extinctions and extinction order.

Contribution

It introduces a novel approach combining connectance analysis with machine learning to predict species extinctions and stability in ecological food webs.

Findings

Trophic efficiency can lead to non-persistent systems.

Simple inequalities of summed interaction strengths predict stability.

Machine learning models accurately forecast extinctions without dynamic simulations.

Abstract

For years, a main focus of ecological research has been to better understand the complex dynamical interactions between species which comprise food webs. Using the connectance properties of a widely explored synthetic food web called the cascade model, we explore the behavior of dynamics on Lotka-Volterra ecological systems. We show how trophic efficiency, a staple assumption in mathematical ecology, produces systems which are not persistent. With clustering analysis we show how straightforward inequalities of the summed values of the birth, death, self-regulation and interaction strengths provide insight into which food webs are more enduring or stable. Through these simplified summed values, we develop a random forest model and a neural network model, both of which are able to predict the number of extinctions that would occur without the need to simulate the dynamics. To conclude, we highlight the variable that plays the dominant role in determining the order in which species go extinct.