Empirical Dynamics: A New Paradigm for Understanding and Managing Species and Ecosystems in a Non-Stationary Nonlinear World

This project was aimed to develop empirical dynamic modeling (EDM) as a practical framework for studying and managing ecosystems, specifically developing technical capacity to build mechanistic models that can confidently forecast environmental futures in a dynamically changing world. To this end, the project demonstrated real solutions for understanding environmental futures in two tactical case studies. In the first case, EDM analysis untangled causal drivers of harmful algal blooms in the Southern California bight, demonstrating short-term prediction capability where none had seemed possible. The second case study developed a predictive framework for conservation management of reef areas in the U.S. Pacific Islands. This required developing new EDM methods for cases where historical time series are short. Together they have grown the predictive data science methods to address non-stationary, non-analogue futures and serve as road maps for new practitioners and applications to understand.