Abstract
Biomonitoring ecosystems is necessary in order to evaluate risks and to efficiently manage ecosystems and their associated services. Agrosystems are the target of multiple stressors that can affect many species through effects cascading along food webs. However, classic biomonitoring, focused on species diversity or indicator species, might be a poor predictor of the risk of such whole-ecosystem perturbations. Thanks to high-throughput sequencing methods, however, it might be possible to obtain sufficient information about entire ecological communities to infer the functioning of their associated interaction networks, and thus monitor more closely the risk of the collapse of entire food webs due to external stressors. In the course of the ‘next-generation biomonitoring’ project, we collectively sought to experiment with this idea of inferring ecological networks on the basis of metabarcoding information gathered on different systems. We here give an overview of issues and preliminary results associated with this endeavour and highlight the main difficulties that such next-generation biomonitoring is still facing. Going from sampling protocols up to methods for comparing inferred networks, through biomolecular, bioinformatic, and network inference, we review all steps of the process, with a view towards generality and transferability towards other systems.