RT Journal Article SR Electronic T1 Data-based, synthesis-driven: setting the agenda for computational ecology JF bioRxiv FD Cold Spring Harbor Laboratory SP 150128 DO 10.1101/150128 A1 Timothée Poisot A1 Richard Labrie A1 Erin Larson A1 Anastasia Rahlin YR 2017 UL http://biorxiv.org/content/early/2017/06/14/150128.abstract AB 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. We discuss how training can be amended to improve the computational literacy of ecologists can be improved.