Abstract
Computational ecology, defined as the application of computational thinking to ecological problems, has the potential to transform the way ecologists think about the integration of data and models. As the practice is gaining prominence as a way to conduct ecological research, it is important to reflect on what its agenda could be, and how it fits within the broader landscape. In this contribution, we suggest areas in which empirical ecologists, modellers, and the emerging community of computational ecologists could engage in a constructive dialogue to build on one another expertise; specifically, about the need to make predictions from models actionable, about the best standards to represent ecological data, and about the proper ways to credit data collection and data reuse. We discuss how training can be amended to improve computational literacy.