Résumé
Biological invasions exert multiple pervasive effects on ecosystems, potentially disrupting species interactions and global ecological processes. Our ability to successfully predict and manage the ecosystem-level impacts of biological invasions is strongly dependent on our capacity to empirically characterize complex biological interactions and their spatiotemporal dynamics. In this chapter, we argue that the comprehensive integration of multiple complementary tools within the explicit context of ecological networks is essential for providing mechanistic insight into invasion processes and their impact across organizational levels. We provide an overview of traditional (stable isotopes, populations genetics) and emerging (metabarcoding, citizen science) techniques and methods, and their practical implementation in the context of biological invasions. We also present several currently available models and machine-learning approaches that could be used for predicting novel or undocumented interactions, thus allowing a more robust and cost-effective forecast of network and ecosystem stability. Finally, we discuss the importance of methodological advancements on the emergence of scientific and societal challenges for investigating local and global species histories with several skill sets.