Self-organized criticality in complex model ecosystems

TL;DR

This paper demonstrates that complex multi-species ecosystems modeled with spatial Lotka-Volterra dynamics naturally develop scale-free correlations in population sizes, driven by near-critical extinction dynamics and described by mean-field theory.

Contribution

It introduces a dynamical mean-field framework for multi-species ecosystems showing scale-free correlations emerge near extinction thresholds.

Findings

Population size correlations are scale-free in large ecosystems.

Near-extinction dynamics induce long-range correlations.

Correlation exponents relate to directed percolation in 3D.

Abstract

We show that spatial extensions of many-species population dynamics models, such as the Lotka-Volterra model with random interactions we focus on in this work, generically exhibit scale-free correlation functions of population sizes in the limit of an infinite number of species. Using dynamical mean-field theory, we describe the many-species system in terms of single-species dynamics with demographic and environmental noises. We show that the single-species model features a random mass term, or equivalently a random space-time averaged growth rate, poising some species very close to extinction. This introduces a hierarchy of ever larger correlation times and lengths as the extinction threshold is approached. In turn, every species, even those far from extinction, are coupled to these near-critical fields which combine to make fluctuations of population sizes generically scale-free. We argue that these correlations are described by exponents derived from those of directed percolation in spatial dimension $d=3$, but not in lower dimensions.