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Type: Journal article
Title: Outstanding challenges in the transferability of ecological models
Author: Yates, K.
Bouchet, P.
Caley, M.
Mengersen, K.
Randin, C.
Parnell, S.
Fielding, A.
Bamford, A.
Ban, S.
Barbosa, A.
Dormann, C.
Elith, J.
Embling, C.
Ervin, G.
Fisher, R.
Gould, S.
Graf, R.
Gregr, E.
Halpin, P.
Heikkinen, R.
et al.
Citation: Trends in Ecology and Evolution, 2018; 33(10):790-802
Publisher: Elsevier
Issue Date: 2018
ISSN: 0169-5347
Statement of
Katherine L. Yates ... Alice R. Jones ... et al.
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.
Keywords: Predictive modeling; extrapolation; generality; habitat models; model transfers; species distribution models; uncertainty
Rights: © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( 4.0/).
RMID: 0030097135
DOI: 10.1016/j.tree.2018.08.001
Grant ID:
Appears in Collections:Earth and Environmental Sciences publications

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