Networks on Networks: Self-organized patterns in meta food webs

A current hot topic of research in network science are multi-layer networks. However, research on these systems, which can be thought of as networks of networks, is still held back by the need for data on concrete applications. An interesting example of networks on networks is provided by ecological meta-foodwebs. Ecological foodwebs, the networks of who eats who in ecology, have been studied for a long time. But, classical food web models do not consider the spatial distribution of species. Most natural environments are far from homogeneous but form distinct patches (e.g. systems of islands, lakes, parks in a city, or patches of forest in an agricultural landscape). The spread of a population in such patchy landscapes is captured by so-called meta-population models. In the past 4 years a number of publications have begun to appear which study the dynamics of foodwebs in patchy landscapes, so-called meta-foodwebs. This has created a situation where strong synergies can be exploited. On the one hand, ecology can profit from newly-developed mathematical tools for the study of multilayer networks, which are currently emerging in network science. On the other hand, for network science meta-foodwebs provide an invaluable real-world example.

This project is part of a larger research unit "For 1748: Networks on Networks", which focuses on the study of meta-foodwebs from a networks perspective. In particular the research unit will develop early warning signals that can be used to monitor real-world meta-foodwebs and warn of impending collapse. Furthermore research will reveal sources of spatial, temporal, and spatiotemporal heterogeneity in these networks. This is interesting from a mathematical/physical perspective, as the central drivers of pattern formation are phenomena such as Anderson Localization and Turing instabilities, which have not been studied in dynamic multi-layer networks. The result of these investiagtions is also expected to be highly relevant for ecology as spacial and temporal heterogeneities are thought to be central drivers for biodiversity and ecosystem functioning.

This project will advance the state of the art of network science by developing new approaches and algorithms for dynamic multilayer networks. Moreover it will establish ecological meta-foodwebs as a paradigmatic class of real world systems that form dynamic multilayer networks. It will thus greatly enrich research in this field by providing a much-needed real world examples of dynamic multi-layer networks in the real world.

The project will create impact in ecology by advancing the understanding of the functioning of meta-foodwebs. In particular the project will deliver new insights into the formation of spatial, temporal and spatio-temporal patterns in these systems, which are essential for biodiversity maintenance. In particular we seek to reveal the impact of spatial habitat structure on the dynamics, which will be valuable for planning decisions and thus can have an impact on policy. Furthermore, we will develop an algorithms that can be used to provide early warning of impending critical transitions and thus aid in safeguarding endangered ecosystems.

The approaches and algorithms developed in this project will be applicable to the vast range of systems that can be described as dynamic multi-layer networks. In particular we see plentiful applications in the monitoring of technical systems, such as large IT systems.