Recent advances in data science are revolutionizing how ecologists explore and interpret complex ecosystem dynamics. This symposium provides an overview of the evolution of data-driven approaches in ecology—from traditional statistical and mechanistic modeling to modern artificial intelligence (AI) techniques. Special focus will be placed on time-series analysis methods, which offer new possibilities for integrating heterogeneous ecological and environmental data to enhance forecasting and system-level understanding. Beyond analytical methods, we will emphasize the growing importance of global ecological datasets—including biodiversity monitoring networks, animal-borne recording, remote sensing, and open-access repositories—that enable large-scale synthesis and model training. By linking global data infrastructures with advanced modeling frameworks, this talk will illustrate how the convergence of data-centric and model-centric paradigms is transforming ecological prediction, knowledge, and applications for ecosystem management.

Commentators: Momoko Ichinokawa (Fish Res & Educ Agency) and Shin-Ichiro Matsuzaki (NIES)