Generalized Lotka-Volterra model with hierarchical interactions

In the analysis of complex ecosystems it is common to use random interaction coefficients, which are often assumed to be such that all species are statistically equivalent. In this work we relax this assumption by imposing hierarchical interspecies interactions. These are incorporated into a generalized Lotka-Volterra dynamical system. In a hierarchical community species benefit more, on average, from interactions with species further below them in the hierarchy than from interactions with those above. Using dynamic mean-field theory, we demonstrate that a strong hierarchical structure is stabilizing, but that it reduces the number of species in the surviving community, as well as their abundances. Additionally, we show that increased heterogeneity in the variances of the interaction coefficients across positions in the hierarchy is destabilizing. We also comment on the structure of the surviving community and demonstrate that the abundance and probability of survival of a species are dependent on its position in the hierarchy.

Automated summary

In the analysis of complex ecosystems, random interaction coefficients are commonly used, assuming that all species are statistically equivalent. This study relaxes this assumption by introducing hierarchical interspecies interactions into a generalized Lotka-Volterra dynamical system. In a hierarchical community, species benefit more from interactions with species lower in the hierarchy than from those higher. Using dynamic mean-field theory, it is demonstrated that a strong hierarchical structure stabilizes the ecosystem, but reduces the number and abundances of surviving species. Additionally, increased heterogeneity in the variances of interaction coefficients across hierarchy positions destabilizes the ecosystem. The study also explores the dependence of species' abundance and survival probability on their position in the hierarchy.