Causal links indicating ecosystem functioning in food webs.

Understanding the complexity of food webs is crucial for assessing ecosystem dynamics. However, the high number of trophic interactions in typical food webs complicates the identification of clear top-down or bottom-up regulatory effects. In this study, we applied interaction asymmetry analysis to examine causality within food webs, allowing for the identification of critical interactions based on topological importance (TI) metrics, which incorporate indirect interactions. We evaluated this method using 34 food web models from the Ecobase database and compared its performance with widely used network metrics. By constructing asymmetry graphs, we transformed original, undirected binary food webs into directed networks highlighting strong causal interactions. Pairwise correlation analyses revealed that ecosystems with higher total biomass indicated stronger bottom-up causal links and greater consumer diversity. Our findings suggest that asymmetry-based metrics provide robust quantitative indicators of causality, offering a straightforward yet powerful tool for assessing ecosystem functioning and health. Supplementary Information: The online version contains supplementary material available at 10.1007/s42974-025-00267-0.