A theoretical approach to understand spatial organization in complex ecologies

TL;DR

This paper introduces a theoretical framework for analyzing spatial organization and dynamics in complex ecological networks, enabling detailed predictions of space-time patterns and relationships among species.

Contribution

It develops a novel method to derive inter-species relationship matrices from adjacency matrices, applicable to spatial ecological modeling.

Findings

Theoretical predictions match numerical simulations across various cases.

The method reveals detailed space-time pattern formations.

Provides a new tool for understanding ecological hierarchies and correlations.

Abstract

Predicting the fate of ecologies is a daunting, albeit extremely important, task. As part of this task one needs to develop an understanding of the organization, hierarchies, and correlations among the species forming the ecology. Focusing on complex food networks we present a theoretical method that allows to achieve this understanding. Starting from the adjacency matrix the method derives specific matrices that encode the various inter-species relationships. The full potential of the method is achieved in a spatial setting where one obtains detailed predictions for the emerging space-time patterns. For a variety of cases these theoretical predictions are verified through numerical simulations.