Ecologists often use "prediction" and "explanation" as near synonyms; after all, if one can explain the processes driving an ecological system one ought to be able to predict its future behavior, and vice versa. However, there is a surprising tension between these goals reflected across many different areas of science, most famously by Breiman (2001) in statistics and Peters (1991) in ecology. The predictive (algorithmic) approach, using statistical tools such as random forests, support vector machines, or classification and regression trees, emphasizes computational tractability and robustness; the explanatory (model-based) approach, using tools as such as maximum likelihood estimation and multilevel modeling, emphasizes interpretability and connection with ecological theory. With the emerging use of big ecological data sets from NEON, remote sensing, telemetry, genomics, and citizen science (among other sources), and with the ever-growing need for practical answers in conservation and management highlighted by the Meeting Theme, the pendulum seems to be swinging toward prediction. This symposium aims to bring together practitioners from across the spectrum of approaches to discuss how we can best achieve a fusion in ecological statistics that provides a good combination of tractability, robustness, interpretability, and relevance to ecological theory. Breiman, Leo. 2001. “Statistical Modeling: The Two Cultures.” Statistical Science 16 (3) (August): 199-215. http://www.jstor.org/stable/2676681. Peters, Robert Henry. 1991. A Critique for Ecology. Cambridge University Press.