Ecological communities, and especially metacommunities, are complex and dynamic entities. Resolving the processes and mechanisms that shape these systems remains a central challenge in ecology. This challenge is compounded by the increasing entanglement of mechanisms, processes, and emergent patterns of biodiversity as scales of space, time, and biological organization expand. Here, we define and contextualize key issues, describe recent progress, and identify remaining challenges in interpreting basic metacommunity data and using predictive models to link processes to patterns. We identify two contrasting modeling strategies for complex metacommunities – top-down and bottom-up – and consider how they guide different approaches to pattern-to-process inference. We find substantial progress in connecting pattern and process through improved data repeatability and scaling, enhanced analytical tools to quantify patterns, and increasingly sophisticated theoretical models that address ecological complexity. However, accurately matching observable patterns with process-oriented theory remains a persistent challenge. Finally, we identify potential pipelines connecting process and pattern and highlight areas for future progress.