Reconstructing large interaction networks from empirical time series data
Journal
Ecology Letters
Journal Volume
24
Journal Issue
12
Pages
2763-2774
Date Issued
2021
Author(s)
Abstract
Reconstructing interactions from observational data is a critical need for investigating natural biological networks, wherein network dimensionality is usually high. However, these pose a challenge to existing methods that can quantify only small interaction networks. Here, we proposed a novel approach to reconstruct high-dimensional interaction Jacobian networks using empirical time series without specific model assumptions. This method, named “multiview distance regularised S-map,” generalised the state space reconstruction to accommodate high dimensionality and overcome difficulties in quantifying massive interactions with limited data. When evaluating this method using time series generated from theoretical models involving hundreds of interacting species, estimated strengths of interaction Jacobians were in good agreement with theoretical expectations. Applying this method to a natural bacterial community helped identify important species from the interaction network and revealed mechanisms governing the dynamical stability of a bacterial community. The proposed method overcame the challenge of high dimensionality in large natural dynamical systems. ? 2021 John Wiley & Sons Ltd.
Subjects
dynamical stability
interaction network
microbial community
network topology
bacterium
empirical analysis
numerical model
reconstruction
time series analysis
Bacteria (microorganisms)
theoretical model
Models, Theoretical
Publisher
John Wiley and Sons Inc
Type
journal article