Title: Outstanding challenges in the transferability of ecological models
Authors : Yates, Katherine L.
Bouchet, Phil J.
Caley, M Julian
Mengersen, Kerrie
Randin, Christophe F.
Parnell, Stephen
Fielding, Alan H.
Bamford, Andrew J.
Ban, Stephen
Barbosa, A Márcia
Dormann, Carsten F.
Elith, Jane
Embling, Clare B.
Ervin, Gary N.
Fisher, Rebecca
Gould, Susan
Graf, Roland Felix
Gregr, Edward J.
Halpin, Patrick N.
Heikkinen, Risto K.
Heinänen, Stefan
Jones, Alice R.
Krishnakumar, Periyadan K.
Lauria, Valentina
Lozano-Montes, Hector
Mannocci, Laura
Mellin, Camille
Mesgaran, Mohsen B.
Moreno-Amat, Elena
Mormede, Sophie
Novaczek, Emilie
Oppel, Steffen
Ortuño Crespo, Guillermo
Peterson, A. Townsend
Rapacciuolo, Giovanni
Roberts, Jason J.
Ross, Rebecca E.
Scales, Kylie L.
Schoeman, David
Snelgrove, Paul
Sundblad, Göran
Thuiller, Wilfried
Torres, Leigh G.
Verbruggen, Heroen
Wang, Lifei
Wenger, Seth
Whittingham, Mark J.
Zharikov, Yuri
Zurell, Damaris
Sequeira, Ana M. M.
Published in : Trends in Ecology & Evolution
Volume(Issue) : 33
Issue : 10
Pages : 790
Pages to: 802
Publisher / Ed. Institution : Elsevier
Issue Date: 2018
License (according to publishing contract) : Licence according to publishing contract
Type of review: Peer review (publication)
Language : English
Subjects : Predictive modeling; Extrapolation; Generality; Habitat models; Model transfers; Species distribution models; Uncertainty
Subject (DDC) : 577: Ecology
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.
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Natural Resource Sciences (IUNR)
Publication type: Article in scientific journal
DOI : 10.1016/j.tree.2018.08.001
ISSN: 1872-8383
0169-5347
URI: https://digitalcollection.zhaw.ch/handle/11475/14620
Appears in Collections:Publikationen Life Sciences und Facility Management

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