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Data-based, synthesis-driven: setting the agenda for computational ecologyuse asterix (*) to get italics
Timothée Poisot, Richard Labrie, Erin Larson, Anastasia RahlinPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
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, modelers, 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.
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NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).
Meta-analyses, Statistical ecology, Theoretical ecology
No need for them to be recommenders of PCIEcology. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct
e.g. John Doe []
2018-02-05 20:51:41
Phillip P.A. Staniczenko