Breakdown of random-matrix universality in persistent Lotka--Volterra communities

Abstract

The eigenvalue spectrum of a random matrix often only depends on the first and second moments of its elements, but not on the specific distribution from which they are drawn. The validity of this universality principle is often assumed without proof in applications. In this letter, we offer a pertinent counterexample in the context of the generalised Lotka--Volterra equations. Using dynamic mean-field theory, we derive the statistics of the interactions between species in an evolved ecological community. We then show that the full statistics of these interactions, beyond those of a Gaussian ensemble, are required to correctly predict the eigenvalue spectrum and therefore stability. Consequently, the universality principle fails in this system. We thus show that the eigenvalue spectra of random matrices can be used to deduce the stability of `feasible' ecological communities, but only if the emergent non-Gaussian statistics of the interactions between species are taken into account.

27 pages, 7 figures

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• Competitive exclusion and Hebbian couplings in random generalised Lotka-Volterra systems
• Dynamical Theory for Adaptive Systems
• Interaction-correlated random matrices
• Critical behavior of a phase transition in the dynamics of interacting populations
• Interplay between intraspecific suppression and environment in shaping biodiversity