Speaker
Description
Foodwebs, as interaction networks of species, are fundamental to understanding ecosystem function. To date, their complex structure and dynamics have been the subject of intensive research. However, most studies have focused on specific properties such as degree distributions, connectance, or species traits in order to identify universal patterns across foodwebs. Holistic investigations that go beyond these specific properties are still lacking.
Here we present a novel analytical workflow based on normalized Laplacian spectra and dimensionality reduction to analyze foodweb topology in a more holistic manner. Laplacian spectra, a well‑established descriptor in network science, have rarely been applied in ecological network research. Our proposed workflow provides an interpretative framework and shows initial indications that it enables (i) the description of generic topological patterns, (ii) the inference of plausible development trajectories, and (iii) the assessment of sampling artifacts across empirical foodwebs.
Our results demonstrate that normalized Laplacian spectra are a powerful tool for the holistic analysis of foodweb topology. Moreover, our proposed workflow could offer a promising blueprint to study other complex, data‑driven networks in ecology and beyond.