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Review article2018Peer reviewedOpen access

Outstanding Challenges in the Transferability of Ecological Models

Yates, Katherine L.; Bouchet, Phil J.; Caley, M. Julian; Mengersen, Kerrie; Randin, Christophe F.; Parnell, Stephen; Fielding, Alan H.; Bamford, Andrew J.; Ban, Stephen; Marcia Barbosa, A.; Dormann, Carsten F.; Elith, Jane; Embling, Clare B.; Ervin, Gary N.; Fisher, Rebecca; Gould, Susan; Graf, Roland F.; Gregr, Edward J.; Halpin, Patrick N.; Heikkinen, Risto K.;
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Abstract

Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions.

Published in

Trends in ecology & evolution
2018, Volume: 33, number: 10, pages: 790-802

    Sustainable Development Goals

    SDG13 Take urgent action to combat climate change and its impacts

    UKÄ Subject classification

    Ecology

    Publication identifier

    DOI: https://doi.org/10.1016/j.tree.2018.08.001

    Permanent link to this page (URI)

    https://res.slu.se/id/publ/96970