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Outstanding Challenges in the Transferability of Ecological Models

Lookup NU author(s): Professor Mark Whittingham

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Abstract

© 2018 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.


Publication metadata

Author(s): Yates KL, Bouchet PJ, Caley MJ, Mengersen K, Randin CF, Parnell S, Fielding AH, Bamford AJ, Ban S, Barbosa AM, Dormann CF, Elith J, Embling CB, Ervin GN, Fisher R, Gould S, Graf RF, Gregr EJ, Halpin PN, Heikkinen RK, Heinanen S, Jones AR, Krishnakumar PK, Lauria V, Lozano-Montes H, Mannocci L, Mellin C, Mesgaran MB, Moreno-Amat E, Mormede S, Novaczek E, Oppel S, Ortuno Crespo G, Peterson AT, Rapacciuolo G, Roberts JJ, Ross RE, Scales KL, Schoeman D, Snelgrove P, Sundblad G, Thuiller W, Torres LG, Verbruggen H, Wang L, Wenger S, Whittingham MJ, Zharikov Y, Zurell D, Sequeira AMM

Publication type: Article

Publication status: Published

Journal: Trends in Ecology and Evolution

Year: 2018

Volume: 33

Issue: 10

Pages: 790-802

Print publication date: 01/10/2018

Online publication date: 27/08/2018

Acceptance date: 02/04/2018

Date deposited: 28/09/2018

ISSN (print): 0169-5347

ISSN (electronic): 1872-8383

Publisher: Elsevier Ltd

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

DOI: 10.1016/j.tree.2018.08.001


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Funding

Funder referenceFunder name
17-8130-0570-CA
Australian Government's National Environmental Science Programme
CA-16-04
DE170100841
DO 786/10-1
CE11001000104
DE140100701
IF/00266/2013/CP1168/CT0001
N62470-15-8003
PZ00P3_168136/1
ZU 361/1-1
UWA/AIMS/CSIRO

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