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Outstanding challenges in the transferability of ecological models

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Yates_2018_Outstanding_challenges_TEE_790.pdf (2.293Mb)
Date
10/2018
Author
Yates, Katherine L.
Bouchet, Phil J.
Caley, M. Julian
Mengersen, Kerrie
Randin, Christophe F.
Parnell, Stephen
Fielding, Alan H.
Bamford, Andrew J.
Ban, Stephen
Márcia 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.
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
Crespo, Guillermo Ortuño
Townsend Peterson, A.
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.
Keywords
Species distribution model
Climate-change
Decision-making
Temporal transferability
Biotic interactions
Predictive models
Habitat selection
Distributions
NIche
Abundance
GE Environmental Sciences
QA Mathematics
QH301 Biology
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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.
Citation
Yates , K L , Bouchet , P J , Caley , M J , Mengersen , K , Randin , C F , Parnell , S , Fielding , A H , Bamford , A J , Ban , S , Márcia Barbosa , A , Dormann , C F , Elith , J , Embling , C B , Ervin , G N , Fisher , R , Gould , S , Graf , R F , Gregr , E J , Halpin , P N , Heikkinen , R K , Heinänen , S , Jones , A R , Krishnakumar , P K , Lauria , V , Lozano-Montes , H , Mannocci , L , Mellin , C , Mesgaran , M B , Moreno-Amat , E , Mormede , S , Novaczek , E , Oppel , S , Crespo , G O , Townsend Peterson , A , Rapacciuolo , G , Roberts , J J , Ross , R E , Scales , K L , Schoeman , D , Snelgrove , P , Sundblad , G , Thuiller , W , Torres , L G , Verbruggen , H , Wang , L , Wenger , S , Whittingham , M J , Zharikov , Y , Zurell , D & Sequeira , A M M 2018 , ' Outstanding challenges in the transferability of ecological models ' , Trends in Ecology and Evolution , vol. 33 , no. 10 , pp. 790-802 . https://doi.org/10.1016/j.tree.2018.08.001
Publication
Trends in Ecology and Evolution
Status
Peer reviewed
DOI
https://doi.org/10.1016/j.tree.2018.08.001
ISSN
0169-5347
Type
Journal item
Rights
Copyright © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Collections
  • University of St Andrews Research
URI
http://hdl.handle.net/10023/19222

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