Abstract
Rethinking the ecological significance of network topology in the area of environmental DNA (eDNA) data represents a major challenge in ecology, as network structure fundamentally influences ecosystem functioning. However, translating network analysis into tools for ecological assessment is still in its infancy. Here, we present an innovative approach integrating eDNA inventories with graph theory (specifically graphlet analysis), niche modelling, and machine learning to build a predictive model for ecological assessment. We validated our approach using 599 phytoplankton inventories collected from 186 lakes spanning a gradient of human‐induced eutrophication. We demonstrated the effectiveness of graphlet‐based network topological metrics for ecological assessment (explaining 67% of the anthropogenic gradient variability), as well as their link with ecological processes. This framework offers a scalable and transposable approach to a wide range of ecological situations to obtain assessment tools grounded on graphlet‐based network topology, paving the way for a more comprehensive understanding of ecosystem functioning.