Mechanistic understanding and forecasting are important for effective policy and management recommendations for ecosystems. However, these tasks are challenging, because real world is complex, where correlation does not necessarily imply causation. Here, I present a time-series analytical framework, known as Empirical Dynamic Modeling (EDM). EDM enables detecting causality among interacting components in nonlinear dynamical systems, constructing time-varying interaction networks, forecasting effects of external forcing, and serving as early warning signal for critical transition. I will demonstrate the efficacy of EDM in various systems. The information can shed light on identifying drivers of ecosystem stability and translating this science into policy-relevant information.