Global environmental change is a pressing issue as evidenced by the rise of extreme weather conditions in many parts of the world, threatening the survival of vulnerable species and habitats. Effective monitoring of climatic and anthropogenic impacts is therefore critical to safeguarding ecosystems, and it would allow us to better understand their response to stressors and predict long-term impacts. Ecological networks provide a biomonitoring framework for examining the system-level response and functioning of an ecosystem, but have been, until recently, constrained by limited empirical data due to the laborious nature of their construction. Hence, most experimental designs have been confined to a single network or a small number of replicate networks, resulting in statistical uncertainty, low resolution, limited spatiotemporal scale and oversimplified assumptions.Advances in data sampling and curation methodologies, such as next-generation sequencing (NGS) and the Internet 'Cloud', have facilitated the emergence of the 'Big Data' phenomenon in Ecology, enabling the construction of ecological networks to be carried out effectively and efficiently. This provides to ecologists an excellent opportunity to expand the way they study ecological networks. In particular, highly replicated networks are now within our grasp if new NGS technologies are combined with machine learning to develop network building methods. A replicated network approach will allow temporal and spatial variations embedded in the data to be taken into consideration, overcoming the limitations in the current 'single network' approach.We are still at the embryonic stage in exploring replicated networks, and with these new opportunities we also face new challenges. In this chapter, we discuss some of these challenges and highlight potential approaches that will help us build and analyse replicated networks to better understand how complex ecosystems operate, and the services and functioning they provide, paving the way for deciphering ecological big data reliably in the future.